import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE      ,   unittest.TestCase				):
							_UpperCAmelCase						    =     KandinskyImgaImgPipeline
							_UpperCAmelCase						    =     ['prompt', 'image_embeds', 'negative_image_embeds', 'image']
							_UpperCAmelCase						    =     [
							    'prompt',
							    'negative_prompt',
							    'image_embeds',
							    'negative_image_embeds',
							    'image',
							]
							_UpperCAmelCase						    =     [
							    'generator',
							    'height',
							    'width',
							    'strength',
							    'guidance_scale',
							    'negative_prompt',
							    'num_inference_steps',
							    'return_dict',
							    'guidance_scale',
							    'num_images_per_prompt',
							    'output_type',
							    'return_dict',
							]
							_UpperCAmelCase						    =     False







							@property
							def 				snake_case  (				self     :    Optional[int]		):
										return 32







							@property
							def 				snake_case  (				self     :    Union[str, Any]		):
										return 32







							@property
							def 				snake_case  (				self     :    Union[str, Any]		):
										return self.time_input_dim







							@property
							def 				snake_case  (				self     :    List[str]		):
										return self.time_input_dim * 4







							@property
							def 				snake_case  (				self     :    List[str]		):
										return 100







							@property
							def 				snake_case  (				self     :    List[str]		):
										lowerCamelCase						:int						  =     XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''		)
										return tokenizer







							@property
							def 				snake_case  (				self     :    int		):
										torch.manual_seed(0		)
										lowerCamelCase						:Union[str, Any]						  =     MCLIPConfig(
										    numDims=self.cross_attention_dim	,					transformerDimensions=self.text_embedder_hidden_size	,					hidden_size=self.text_embedder_hidden_size	,					intermediate_size=37	,					num_attention_heads=4	,					num_hidden_layers=5	,					vocab_size=1005	,					)

										lowerCamelCase						:Tuple						  =     MultilingualCLIP(__snake_case		)
										lowerCamelCase						:Dict						  =     text_encoder.eval()

										return text_encoder







							@property
							def 				snake_case  (				self     :    Optional[Any]		):
										torch.manual_seed(0		)

										lowerCamelCase						:Tuple						  =     {
										    '''in_channels''': 4,
										    # Out channels is double in channels because predicts mean and variance
										    '''out_channels''': 8,
										    '''addition_embed_type''': '''text_image''',
										    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
										    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
										    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
										    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
										    '''layers_per_block''': 1,
										    '''encoder_hid_dim''': self.text_embedder_hidden_size,
										    '''encoder_hid_dim_type''': '''text_image_proj''',
										    '''cross_attention_dim''': self.cross_attention_dim,
										    '''attention_head_dim''': 4,
										    '''resnet_time_scale_shift''': '''scale_shift''',
										    '''class_embed_type''': None,
										}

										lowerCamelCase						:Dict						  =     UNetaDConditionModel(**__snake_case		)
										return model







							@property
							def 				snake_case  (				self     :    Dict		):
										return {
										    "block_out_channels": [32, 64],
										    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
										    "in_channels": 3,
										    "latent_channels": 4,
										    "layers_per_block": 1,
										    "norm_num_groups": 8,
										    "norm_type": "spatial",
										    "num_vq_embeddings": 12,
										    "out_channels": 3,
										    "up_block_types": [
										        "AttnUpDecoderBlock2D",
										        "UpDecoderBlock2D",
										    ],
										    "vq_embed_dim": 4,
										}







							@property
							def 				snake_case  (				self     :    int		):
										torch.manual_seed(0		)
										lowerCamelCase						:Union[str, Any]						  =     VQModel(**self.dummy_movq_kwargs		)
										return model







							def 				snake_case  (				self     :    Optional[Any]		):
										lowerCamelCase						:Union[str, Any]						  =     self.dummy_text_encoder
										lowerCamelCase						:Any						  =     self.dummy_tokenizer
										lowerCamelCase						:Optional[int]						  =     self.dummy_unet
										lowerCamelCase						:Union[str, Any]						  =     self.dummy_movq

										lowerCamelCase						:int						  =     {
										    '''num_train_timesteps''': 1000,
										    '''beta_schedule''': '''linear''',
										    '''beta_start''': 0.0_0_0_8_5,
										    '''beta_end''': 0.0_1_2,
										    '''clip_sample''': False,
										    '''set_alpha_to_one''': False,
										    '''steps_offset''': 0,
										    '''prediction_type''': '''epsilon''',
										    '''thresholding''': False,
										}

										lowerCamelCase						:List[str]						  =     DDIMScheduler(**__snake_case		)

										lowerCamelCase						:Tuple						  =     {
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''movq''': movq,
										}

										return components







							def 				snake_case  (				self     :    Tuple	,					__snake_case     :    str	,					__snake_case     :    Tuple=0		):
										lowerCamelCase						:Optional[int]						  =     floats_tensor((1, self.cross_attention_dim)	,					rng=random.Random(__snake_case		)		).to(__snake_case		)
										lowerCamelCase						:str						  =     floats_tensor((1, self.cross_attention_dim)	,					rng=random.Random(seed + 1		)		).to(__snake_case		)
										# create init_image
										lowerCamelCase						:List[Any]						  =     floats_tensor((1, 3, 64, 64)	,					rng=random.Random(__snake_case		)		).to(__snake_case		)
										lowerCamelCase						:Optional[int]						  =     image.cpu().permute(0	,					2	,					3	,					1		)[0]
										lowerCamelCase						:Optional[int]						  =     Image.fromarray(np.uinta(__snake_case		)		).convert('''RGB'''		).resize((256, 256)		)

										if str(__snake_case		).startswith('''mps'''		):
													lowerCamelCase						:Any						  =     torch.manual_seed(__snake_case		)
										else:
													lowerCamelCase						:Dict						  =     torch.Generator(device=__snake_case		).manual_seed(__snake_case		)
										lowerCamelCase						:Union[str, Any]						  =     {
										    '''prompt''': '''horse''',
										    '''image''': init_image,
										    '''image_embeds''': image_embeds,
										    '''negative_image_embeds''': negative_image_embeds,
										    '''generator''': generator,
										    '''height''': 64,
										    '''width''': 64,
										    '''num_inference_steps''': 10,
										    '''guidance_scale''': 7.0,
										    '''strength''': 0.2,
										    '''output_type''': '''np''',
										}
										return inputs







							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:List[Any]						  =     '''cpu'''

										lowerCamelCase						:List[str]						  =     self.get_dummy_components()

										lowerCamelCase						:Any						  =     self.pipeline_class(**__snake_case		)
										lowerCamelCase						:Union[str, Any]						  =     pipe.to(__snake_case		)

										pipe.set_progress_bar_config(disable=__snake_case		)

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

										lowerCamelCase						:Any						  =     pipe(
										    **self.get_dummy_inputs(__snake_case		)	,					return_dict=__snake_case	,					)[0]

										lowerCamelCase						:Tuple						  =     image[0, -3:, -3:, -1]
										lowerCamelCase						:Dict						  =     image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)

										lowerCamelCase						:Dict						  =     np.array(
										    [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3]		)
										assert (
										    np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
										), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
										assert (
										    np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1e-2
										), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"


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







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







							def 				snake_case  (				self     :    Any		):
										lowerCamelCase						:Union[str, Any]						  =     load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/kandinsky/kandinsky_img2img_frog.npy'''		)

										lowerCamelCase						:int						  =     load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''		)
										lowerCamelCase						:int						  =     '''A red cartoon frog, 4k'''

										lowerCamelCase						:Dict						  =     KandinskyPriorPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-1-prior'''	,					torch_dtype=torch.floataa		)
										pipe_prior.to(__snake_case		)

										lowerCamelCase						:Optional[Any]						  =     KandinskyImgaImgPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-1'''	,					torch_dtype=torch.floataa		)
										lowerCamelCase						:Optional[Any]						  =     pipeline.to(__snake_case		)

										pipeline.set_progress_bar_config(disable=__snake_case		)

										lowerCamelCase						:int						  =     torch.Generator(device='''cpu'''		).manual_seed(0		)
										lowerCamelCase						:int						  =     pipe_prior(
										    __snake_case	,					generator=__snake_case	,					num_inference_steps=5	,					negative_prompt=''''''	,					).to_tuple()

										lowerCamelCase						:int						  =     pipeline(
										    __snake_case	,					image=__snake_case	,					image_embeds=__snake_case	,					negative_image_embeds=__snake_case	,					generator=__snake_case	,					num_inference_steps=100	,					height=768	,					width=768	,					strength=0.2	,					output_type='''np'''	,					)

										lowerCamelCase						:Dict						  =     output.images[0]

										assert image.shape == (768, 768, 3)

										assert_mean_pixel_difference(__snake_case	,					__snake_case		)




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()
A__           =							logging.get_logger(__name__)





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





def  _lowerCamelCase					(   a_				: int):
			lowerCamelCase						:Any						  =     ['''head.weight''', '''head.bias''']
			for k in ignore_keys:
						state_dict.pop(a_						,			a_)





def  _lowerCamelCase					(   a_				: int						,			a_				: Any						,			a_				: Tuple):
			lowerCamelCase						:Optional[Any]						  =     dct.pop(a_)
			lowerCamelCase						:str						  =     val





def  _lowerCamelCase					(   ):
			lowerCamelCase						:Tuple						  =     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			lowerCamelCase						:Tuple						  =     Image.open(requests.get(a_						,			stream=a_).raw)
			return im





@torch.no_grad()
def  _lowerCamelCase					(   a_				: Optional[Any]						,			a_				: Optional[Any]						,			a_				: Optional[Any]=False):
			lowerCamelCase						:Optional[int]						  =     BitConfig(
			    global_padding='''same'''						,			layer_type='''bottleneck'''						,			depths=(3, 4, 9)						,			out_features=['''stage3''']						,			embedding_dynamic_padding=a_						,			)
			lowerCamelCase						:Optional[int]						  =     ViTHybridConfig(backbone_config=a_						,			image_size=3_84						,			num_labels=10_00)
			lowerCamelCase						:List[Any]						  =     False

			# load original model from timm
			lowerCamelCase						:List[str]						  =     timm.create_model(a_						,			pretrained=a_)
			timm_model.eval()

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

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

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

			# create image processor
			lowerCamelCase						:Dict						  =     create_transform(**resolve_data_config({}						,			model=a_))
			lowerCamelCase						:str						  =     transform.transforms

			lowerCamelCase						:int						  =     {
			    '''bilinear''': PILImageResampling.BILINEAR,
			    '''bicubic''': PILImageResampling.BICUBIC,
			    '''nearest''': PILImageResampling.NEAREST,
			}

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

			lowerCamelCase						:Dict						  =     prepare_img()
			lowerCamelCase						:str						  =     transform(a_).unsqueeze(0)
			lowerCamelCase						:str						  =     processor(a_						,			return_tensors='''pt''').pixel_values

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

			# verify logits
			with torch.no_grad():
						lowerCamelCase						:Optional[int]						  =     model(a_)
						lowerCamelCase						:Union[str, Any]						  =     outputs.logits

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

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

			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__":
			A__           =							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."""
			)

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



