
"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 				A__ (			__SCREAMING_SNAKE_CASE ,  __SCREAMING_SNAKE_CASE ,  unittest.TestCase):
		A_		: str            =			CycleDiffusionPipeline
		A_		: Dict            =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
		    'negative_prompt',
		    'height',
		    'width',
		    'negative_prompt_embeds',
		}
		A_		: Optional[Any]            =			PipelineTesterMixin.required_optional_params - {'latents'}
		A_		: Any            =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'})
		A_		: Optional[Any]            =			IMAGE_TO_IMAGE_IMAGE_PARAMS
		A_		: Dict            =			IMAGE_TO_IMAGE_IMAGE_PARAMS

		def 							__lowerCamelCase							(   self				):
			torch.manual_seed(0				)
			__lowerCAmelCase    :		Any		    =   UNetaDConditionModel(
			    block_out_channels=(32, 64) ,	layers_per_block=2 ,	sample_size=32 ,	in_channels=4 ,	out_channels=4 ,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,	up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') ,	cross_attention_dim=32 ,	)
			__lowerCAmelCase    :		str		    =   DDIMScheduler(
			    beta_start=0.0_0085 ,	beta_end=0.012 ,	beta_schedule='scaled_linear' ,	num_train_timesteps=10_00 ,	clip_sample=__snake_case ,	set_alpha_to_one=__snake_case ,	)
			torch.manual_seed(0				)
			__lowerCAmelCase    :		Tuple		    =   AutoencoderKL(
			    block_out_channels=[32, 64] ,	in_channels=3 ,	out_channels=3 ,	down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,	up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,	latent_channels=4 ,	)
			torch.manual_seed(0				)
			__lowerCAmelCase    :		List[str]		    =   CLIPTextConfig(
			    bos_token_id=0 ,	eos_token_id=2 ,	hidden_size=32 ,	intermediate_size=37 ,	layer_norm_eps=1E-05 ,	num_attention_heads=4 ,	num_hidden_layers=5 ,	pad_token_id=1 ,	vocab_size=10_00 ,	)
			__lowerCAmelCase    :		Optional[int]		    =   CLIPTextModel(__snake_case				)
			__lowerCAmelCase    :		Optional[int]		    =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

			__lowerCAmelCase    :		Dict		    =   {
			    '''unet''': unet,
			    '''scheduler''': scheduler,
			    '''vae''': vae,
			    '''text_encoder''': text_encoder,
			    '''tokenizer''': tokenizer,
			    '''safety_checker''': None,
			    '''feature_extractor''': None,
			}
			return components

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=0				):
			__lowerCAmelCase    :		Any		    =   floats_tensor((1, 3, 32, 32) ,	rng=random.Random(__snake_case				)				).to(__snake_case				)
			__lowerCAmelCase    :		int		    =   image / 2 + 0.5
			if str(__snake_case				).startswith('mps'				):
				__lowerCAmelCase    :		Union[str, Any]		    =   torch.manual_seed(__snake_case				)
			else:
				__lowerCAmelCase    :		List[str]		    =   torch.Generator(device=__snake_case				).manual_seed(__snake_case				)
			__lowerCAmelCase    :		str		    =   {
			    '''prompt''': '''An astronaut riding an elephant''',
			    '''source_prompt''': '''An astronaut riding a horse''',
			    '''image''': image,
			    '''generator''': generator,
			    '''num_inference_steps''': 2,
			    '''eta''': 0.1,
			    '''strength''': 0.8,
			    '''guidance_scale''': 3,
			    '''source_guidance_scale''': 1,
			    '''output_type''': '''numpy''',
			}
			return inputs

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Tuple		    =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator

			__lowerCAmelCase    :		int		    =   self.get_dummy_components()
			__lowerCAmelCase    :		Optional[int]		    =   CycleDiffusionPipeline(**__snake_case				)
			__lowerCAmelCase    :		Tuple		    =   pipe.to(__snake_case				)
			pipe.set_progress_bar_config(disable=__snake_case				)

			__lowerCAmelCase    :		int		    =   self.get_dummy_inputs(__snake_case				)
			__lowerCAmelCase    :		Optional[Any]		    =   pipe(**__snake_case				)
			__lowerCAmelCase    :		int		    =   output.images

			__lowerCAmelCase    :		Tuple		    =   images[0, -3:, -3:, -1]

			assert images.shape == (1, 32, 32, 3)
			__lowerCAmelCase    :		Any		    =   np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]				)

			assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2

		@unittest.skipIf(torch_device != 'cuda' ,	'This test requires a GPU'				)
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Union[str, Any]		    =   self.get_dummy_components()
			for name, module in components.items():
				if hasattr(__snake_case ,	'half'				):
					__lowerCAmelCase    :		Tuple		    =   module.half()
			__lowerCAmelCase    :		List[str]		    =   CycleDiffusionPipeline(**__snake_case				)
			__lowerCAmelCase    :		Any		    =   pipe.to(__snake_case				)
			pipe.set_progress_bar_config(disable=__snake_case				)

			__lowerCAmelCase    :		List[Any]		    =   self.get_dummy_inputs(__snake_case				)
			__lowerCAmelCase    :		Any		    =   pipe(**__snake_case				)
			__lowerCAmelCase    :		Dict		    =   output.images

			__lowerCAmelCase    :		List[str]		    =   images[0, -3:, -3:, -1]

			assert images.shape == (1, 32, 32, 3)
			__lowerCAmelCase    :		Optional[int]		    =   np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]				)

			assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2

		@skip_mps
		def 							__lowerCamelCase							(   self				):
			return super().test_save_load_local()

		@unittest.skip('non-deterministic pipeline'				)
		def 							__lowerCamelCase							(   self				):
			return super().test_inference_batch_single_identical()

		@skip_mps
		def 							__lowerCamelCase							(   self				):
			return super().test_dict_tuple_outputs_equivalent()

		@skip_mps
		def 							__lowerCamelCase							(   self				):
			return super().test_save_load_optional_components()







		@skip_mps
		def 							__lowerCamelCase							(   self				):
			return super().test_attention_slicing_forward_pass()




@slow
@require_torch_gpu
class 				A__ (			unittest.TestCase):

		def 							__lowerCamelCase							(   self				):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Union[str, Any]		    =   load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/cycle-diffusion/black_colored_car.png'				)
			__lowerCAmelCase    :		Union[str, Any]		    =   load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'				)
			__lowerCAmelCase    :		Optional[Any]		    =   init_image.resize((5_12, 5_12)				)

			__lowerCAmelCase    :		List[Any]		    =   '''CompVis/stable-diffusion-v1-4'''
			__lowerCAmelCase    :		List[Any]		    =   DDIMScheduler.from_pretrained(__snake_case ,	subfolder='scheduler'				)
			__lowerCAmelCase    :		Dict		    =   CycleDiffusionPipeline.from_pretrained(
			    __snake_case ,	scheduler=__snake_case ,	safety_checker=__snake_case ,	torch_dtype=torch.floataa ,	revision='fp16'				)

			pipe.to(__snake_case				)
			pipe.set_progress_bar_config(disable=__snake_case				)
			pipe.enable_attention_slicing()

			__lowerCAmelCase    :		Union[str, Any]		    =   '''A black colored car'''
			__lowerCAmelCase    :		List[Any]		    =   '''A blue colored car'''

			__lowerCAmelCase    :		Optional[Any]		    =   torch.manual_seed(0				)
			__lowerCAmelCase    :		str		    =   pipe(
			    prompt=__snake_case ,	source_prompt=__snake_case ,	image=__snake_case ,	num_inference_steps=1_00 ,	eta=0.1 ,	strength=0.85 ,	guidance_scale=3 ,	source_guidance_scale=1 ,	generator=__snake_case ,	output_type='np' ,	)
			__lowerCAmelCase    :		Optional[Any]		    =   output.images

			# the values aren't exactly equal, but the images look the same visually
			assert np.abs(image - expected_image				).max() < 5E-1







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[Any]		    =   load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/cycle-diffusion/black_colored_car.png'				)
			__lowerCAmelCase    :		Tuple		    =   load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'				)
			__lowerCAmelCase    :		Tuple		    =   init_image.resize((5_12, 5_12)				)

			__lowerCAmelCase    :		List[str]		    =   '''CompVis/stable-diffusion-v1-4'''
			__lowerCAmelCase    :		Any		    =   DDIMScheduler.from_pretrained(__snake_case ,	subfolder='scheduler'				)
			__lowerCAmelCase    :		Optional[Any]		    =   CycleDiffusionPipeline.from_pretrained(__snake_case ,	scheduler=__snake_case ,	safety_checker=__snake_case				)

			pipe.to(__snake_case				)
			pipe.set_progress_bar_config(disable=__snake_case				)
			pipe.enable_attention_slicing()

			__lowerCAmelCase    :		Optional[int]		    =   '''A black colored car'''
			__lowerCAmelCase    :		Optional[int]		    =   '''A blue colored car'''

			__lowerCAmelCase    :		Optional[Any]		    =   torch.manual_seed(0				)
			__lowerCAmelCase    :		str		    =   pipe(
			    prompt=__snake_case ,	source_prompt=__snake_case ,	image=__snake_case ,	num_inference_steps=1_00 ,	eta=0.1 ,	strength=0.85 ,	guidance_scale=3 ,	source_guidance_scale=1 ,	generator=__snake_case ,	output_type='np' ,	)
			__lowerCAmelCase    :		Optional[Any]		    =   output.images

			assert np.abs(image - expected_image				).max() < 2E-2

"""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)