


import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class   A__ (   snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		UnCLIPImageVariationPipeline
    __magic_name__        =		IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
    __magic_name__        =		IMAGE_VARIATION_BATCH_PARAMS

    __magic_name__        =		[
        'generator',
        'return_dict',
        'decoder_num_inference_steps',
        'super_res_num_inference_steps',
    ]
    __magic_name__        =		False
    @property
    def 		a_      (	self   ):
         return 3_2
    @property
    def 		a_      (	self   ):
         return 3_2
    @property
    def 		a_      (	self   ):
         return self.time_input_dim
    @property
    def 		a_      (	self   ):
         return self.time_input_dim * 4
    @property
    def 		a_      (	self   ):
         return 1_0_0
    @property
    def 		a_      (	self   ):
         snake_case					=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )
         return tokenizer
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )
         snake_case					=    CLIPTextConfig(
             bos_token_id=0      , eos_token_id=2      , hidden_size=self.text_embedder_hidden_size      , projection_dim=self.text_embedder_hidden_size      , intermediate_size=3_7      , layer_norm_eps=1E-05      , num_attention_heads=4      , num_hidden_layers=5      , pad_token_id=1      , vocab_size=1_0_0_0      , )
         return CLIPTextModelWithProjection(__snake_case   )
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )
         snake_case					=    CLIPVisionConfig(
             hidden_size=self.text_embedder_hidden_size      , projection_dim=self.text_embedder_hidden_size      , num_hidden_layers=5      , num_attention_heads=4      , image_size=3_2      , intermediate_size=3_7      , patch_size=1      , )
         return CLIPVisionModelWithProjection(__snake_case   )
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )

         snake_case					=    {
             '''clip_embeddings_dim''': self.text_embedder_hidden_size,
             '''time_embed_dim''': self.time_embed_dim,
             '''cross_attention_dim''': self.cross_attention_dim,
         }

         snake_case					=    UnCLIPTextProjModel(**__snake_case   )
         return model
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )

         snake_case					=    {
             '''sample_size''': 3_2,
             # RGB in channels
             '''in_channels''': 3,
             # Out channels is double in channels because predicts mean and variance
             '''out_channels''': 6,
             '''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,
             '''cross_attention_dim''': self.cross_attention_dim,
             '''attention_head_dim''': 4,
             '''resnet_time_scale_shift''': '''scale_shift''',
             '''class_embed_type''': '''identity''',
         }

         snake_case					=    UNetaDConditionModel(**__snake_case   )
         return model
    @property
    def 		a_      (	self   ):
         return {
             "sample_size": 6_4,
             "layers_per_block": 1,
             "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
             "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
             "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
             "in_channels": 6,
             "out_channels": 3,
         }
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )

         snake_case					=    UNetaDModel(**self.dummy_super_res_kwargs   )
         return model
    @property
    def 		a_      (	self   ):
         # seeded differently to get different unet than `self.dummy_super_res_first`
         torch.manual_seed(1   )

         snake_case					=    UNetaDModel(**self.dummy_super_res_kwargs   )
         return model
    def 		a_      (	self   ):
         snake_case					=    self.dummy_decoder
         snake_case					=    self.dummy_text_proj
         snake_case					=    self.dummy_text_encoder
         snake_case					=    self.dummy_tokenizer
         snake_case					=    self.dummy_super_res_first
         snake_case					=    self.dummy_super_res_last

         snake_case					=    UnCLIPScheduler(
             variance_type='''learned_range'''      , prediction_type='''epsilon'''      , num_train_timesteps=1_0_0_0      , )

         snake_case					=    UnCLIPScheduler(
             variance_type='''fixed_small_log'''      , prediction_type='''epsilon'''      , num_train_timesteps=1_0_0_0      , )

         snake_case					=    CLIPImageProcessor(crop_size=3_2      , size=3_2   )

         snake_case					=    self.dummy_image_encoder

         return {
             "decoder": decoder,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "text_proj": text_proj,
             "feature_extractor": feature_extractor,
             "image_encoder": image_encoder,
             "super_res_first": super_res_first,
             "super_res_last": super_res_last,
             "decoder_scheduler": decoder_scheduler,
             "super_res_scheduler": super_res_scheduler,
         }
    def 		a_      (	self      , __snake_case      , __snake_case=0      , __snake_case=True   ):
         snake_case					=    floats_tensor((1, 3, 3_2, 3_2)      , rng=random.Random(__snake_case   )   ).to(__snake_case   )
         if str(__snake_case   ).startswith('''mps'''   ):
              snake_case					=    torch.manual_seed(__snake_case   )
         else:
              snake_case					=    torch.Generator(device=__snake_case   ).manual_seed(__snake_case   )

         if pil_image:
              snake_case					=    input_image * 0.5 + 0.5
              snake_case					=    input_image.clamp(0      , 1   )
              snake_case					=    input_image.cpu().permute(0      , 2      , 3      , 1   ).float().numpy()
              snake_case					=    DiffusionPipeline.numpy_to_pil(__snake_case   )[0]

         return {
             "image": input_image,
             "generator": generator,
             "decoder_num_inference_steps": 2,
             "super_res_num_inference_steps": 2,
             "output_type": "np",
         }
    def 		a_      (	self   ):
         snake_case					=    '''cpu'''

         snake_case					=    self.get_dummy_components()

         snake_case					=    self.pipeline_class(**__snake_case   )
         snake_case					=    pipe.to(__snake_case   )

         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )

         snake_case					=    pipe(**__snake_case   )
         snake_case					=    output.images

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )

         snake_case					=    pipe(
             **__snake_case      , return_dict=__snake_case      , )[0]

         snake_case					=    image[0, -3:, -3:, -1]
         snake_case					=    image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)

         snake_case					=    np.array(
             [
                 0.9997,
                 0.0002,
                 0.9997,
                 0.9997,
                 0.9969,
                 0.0023,
                 0.9997,
                 0.9969,
                 0.9970,
             ]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
    def 		a_      (	self   ):
         snake_case					=    '''cpu'''

         snake_case					=    self.get_dummy_components()

         snake_case					=    self.pipeline_class(**__snake_case   )
         snake_case					=    pipe.to(__snake_case   )

         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )

         snake_case					=    pipe(**__snake_case   )
         snake_case					=    output.images

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )

         snake_case					=    pipe(
             **__snake_case      , return_dict=__snake_case      , )[0]

         snake_case					=    image[0, -3:, -3:, -1]
         snake_case					=    image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)

         snake_case					=    np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
    def 		a_      (	self   ):
         snake_case					=    '''cpu'''

         snake_case					=    self.get_dummy_components()

         snake_case					=    self.pipeline_class(**__snake_case   )
         snake_case					=    pipe.to(__snake_case   )

         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )
         snake_case					=    [
             pipeline_inputs['''image'''],
             pipeline_inputs['''image'''],
         ]

         snake_case					=    pipe(**__snake_case   )
         snake_case					=    output.images

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )
         snake_case					=    [
             tuple_pipeline_inputs['''image'''],
             tuple_pipeline_inputs['''image'''],
         ]

         snake_case					=    pipe(
             **__snake_case      , return_dict=__snake_case      , )[0]

         snake_case					=    image[0, -3:, -3:, -1]
         snake_case					=    image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (2, 6_4, 6_4, 3)

         snake_case					=    np.array(
             [
                 0.9997,
                 0.9989,
                 0.0008,
                 0.0021,
                 0.9960,
                 0.0018,
                 0.0014,
                 0.0002,
                 0.9933,
             ]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
    def 		a_      (	self   ):
         snake_case					=    torch.device('''cpu'''   )

         class   A__ :







             """simple docstring"""

             __magic_name__        =		1






         snake_case					=    self.get_dummy_components()

         snake_case					=    self.pipeline_class(**__snake_case   )
         snake_case					=    pipe.to(__snake_case   )

         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    torch.Generator(device=__snake_case   ).manual_seed(0   )
         snake_case					=    pipe.decoder.dtype
         snake_case					=    1

         snake_case					=    (
             batch_size,
             pipe.decoder.config.in_channels,
             pipe.decoder.config.sample_size,
             pipe.decoder.config.sample_size,
         )
         snake_case					=    pipe.prepare_latents(
             __snake_case      , dtype=__snake_case      , device=__snake_case      , generator=__snake_case      , latents=__snake_case      , scheduler=DummyScheduler()   )

         snake_case					=    (
             batch_size,
             pipe.super_res_first.config.in_channels // 2,
             pipe.super_res_first.config.sample_size,
             pipe.super_res_first.config.sample_size,
         )
         snake_case					=    pipe.prepare_latents(
             __snake_case      , dtype=__snake_case      , device=__snake_case      , generator=__snake_case      , latents=__snake_case      , scheduler=DummyScheduler()   )

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )

         snake_case					=    pipe(
             **__snake_case      , decoder_latents=__snake_case      , super_res_latents=__snake_case   ).images

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )
         # Don't pass image, instead pass embedding
         snake_case					=    pipeline_inputs.pop('''image'''   )
         snake_case					=    pipe.image_encoder(__snake_case   ).image_embeds

         snake_case					=    pipe(
             **__snake_case      , decoder_latents=__snake_case      , super_res_latents=__snake_case      , image_embeddings=__snake_case      , ).images

         # make sure passing text embeddings manually is identical
         assert np.abs(img_out_a - img_out_a   ).max() < 1E-4
    @skip_mps
    def 		a_      (	self   ):
         snake_case					=    torch_device == '''cpu'''

         # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
         snake_case					=    1E-2

         self._test_attention_slicing_forward_pass(
             test_max_difference=__snake_case      , expected_max_diff=__snake_case   )
    @skip_mps
    def 		a_      (	self   ):
         snake_case					=    torch_device == '''cpu'''
         snake_case					=    True
         snake_case					=    [
             '''decoder_num_inference_steps''',
             '''super_res_num_inference_steps''',
         ]

         self._test_inference_batch_single_identical(
             test_max_difference=__snake_case      , relax_max_difference=__snake_case      , additional_params_copy_to_batched_inputs=__snake_case      , )
    def 		a_      (	self   ):
         snake_case					=    [
             '''decoder_num_inference_steps''',
             '''super_res_num_inference_steps''',
         ]

         if torch_device == "mps":
              # TODO: MPS errors with larger batch sizes
              snake_case					=    [2, 3]
              self._test_inference_batch_consistent(
                  batch_sizes=__snake_case      , additional_params_copy_to_batched_inputs=__snake_case      , )
         else:
              self._test_inference_batch_consistent(
                  additional_params_copy_to_batched_inputs=__snake_case   )
    @skip_mps
    def 		a_      (	self   ):
         return super().test_dict_tuple_outputs_equivalent()
    @skip_mps
    def 		a_      (	self   ):
         return super().test_save_load_local()




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






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







    """simple docstring"""

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




    def 		a_      (	self   ):
         snake_case					=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'''   )
         snake_case					=    load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/unclip/karlo_v1_alpha_cat_variation_fp16.npy'''   )

         snake_case					=    UnCLIPImageVariationPipeline.from_pretrained(
             '''kakaobrain/karlo-v1-alpha-image-variations'''      , torch_dtype=torch.floataa   )
         snake_case					=    pipeline.to(__snake_case   )
         pipeline.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    torch.Generator(device='''cpu'''   ).manual_seed(0   )
         snake_case					=    pipeline(
             __snake_case      , generator=__snake_case      , output_type='''np'''      , )

         snake_case					=    output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)

         assert_mean_pixel_difference(__snake_case      , __snake_case      , 1_5   )








import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_=False    ):



     """simple docstring"""






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

     # projection layer + position embeddings
     rename_keys.extend(
         [
             ('''module.cls_token''', '''vit.embeddings.cls_token'''),
             ('''module.patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
             ('''module.patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
             ('''module.pos_embed''', '''vit.embeddings.position_embeddings'''),
         ]    )

     if base_model:
          # layernorm + pooler
          rename_keys.extend(
              [
                  ('''module.norm.weight''', '''layernorm.weight'''),
                  ('''module.norm.bias''', '''layernorm.bias'''),
              ]    )

          # if just the base model, we should remove "vit" from all keys that start with "vit"
          snake_case					=    [(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'''),
              ]    )

     return rename_keys
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_=False    ):



     """simple docstring"""






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



     """simple docstring"""






     snake_case					=    ['''head.weight''', '''head.bias''']
     for k in ignore_keys:
          state_dict.pop(UpperCamelCase_							,UpperCamelCase_    )
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    [
         '''module.fc.fc1.weight''',
         '''module.fc.fc1.bias''',
         '''module.fc.bn1.weight''',
         '''module.fc.bn1.bias''',
         '''module.fc.bn1.running_mean''',
         '''module.fc.bn1.running_var''',
         '''module.fc.bn1.num_batches_tracked''',
         '''module.fc.fc2.weight''',
         '''module.fc.fc2.bias''',
         '''module.fc.bn2.weight''',
         '''module.fc.bn2.bias''',
         '''module.fc.bn2.running_mean''',
         '''module.fc.bn2.running_var''',
         '''module.fc.bn2.num_batches_tracked''',
         '''module.fc.fc3.weight''',
         '''module.fc.fc3.bias''',
     ]
     for k in ignore_keys:
          state_dict.pop(UpperCamelCase_							,UpperCamelCase_    )
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    dct.pop(UpperCamelCase_    )
     snake_case					=    val
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    ViTMSNConfig()
     snake_case					=    10_00

     snake_case					=    '''datasets/huggingface/label-files'''
     snake_case					=    '''imagenet-1k-id2label.json'''
     snake_case					=    json.load(open(hf_hub_download(UpperCamelCase_							,UpperCamelCase_    )							,'''r'''    )    )
     snake_case					=    {int(UpperCamelCase_    ): v for k, v in idalabel.items()}
     snake_case					=    idalabel
     snake_case					=    {v: k for k, v in idalabel.items()}

     if "s16" in checkpoint_url:
          snake_case					=    3_84
          snake_case					=    15_36
          snake_case					=    6
     elif "l16" in checkpoint_url:
          snake_case					=    10_24
          snake_case					=    40_96
          snake_case					=    24
          snake_case					=    16
          snake_case					=    0.1
     elif "b4" in checkpoint_url:
          snake_case					=    4
     elif "l7" in checkpoint_url:
          snake_case					=    7
          snake_case					=    10_24
          snake_case					=    40_96
          snake_case					=    24
          snake_case					=    16
          snake_case					=    0.1

     snake_case					=    ViTMSNModel(UpperCamelCase_    )

     snake_case					=    torch.hub.load_state_dict_from_url(UpperCamelCase_							,map_location='''cpu'''    )['''target_encoder''']

     snake_case					=    ViTImageProcessor(size=config.image_size    )

     remove_projection_head(UpperCamelCase_    )
     snake_case					=    create_rename_keys(UpperCamelCase_							,base_model=UpperCamelCase_    )

     for src, dest in rename_keys:
          rename_key(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )
     read_in_q_k_v(UpperCamelCase_							,UpperCamelCase_							,base_model=UpperCamelCase_    )

     model.load_state_dict(UpperCamelCase_    )
     model.eval()

     snake_case					=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''

     snake_case					=    Image.open(requests.get(UpperCamelCase_							,stream=UpperCamelCase_    ).raw    )
     snake_case					=    ViTImageProcessor(
         size=config.image_size							,image_mean=UpperCamelCase_							,image_std=UpperCamelCase_    )
     snake_case					=    image_processor(images=UpperCamelCase_							,return_tensors='''pt'''    )

     # forward pass
     torch.manual_seed(2    )
     snake_case					=    model(**UpperCamelCase_    )
     snake_case					=    outputs.last_hidden_state

     # The following Colab Notebook was used to generate these outputs:
     # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
     if "s16" in checkpoint_url:
          snake_case					=    torch.tensor([[-1.0915, -1.4876, -1.1809]]    )
     elif "b16" in checkpoint_url:
          snake_case					=    torch.tensor([[14.2889, -18.9045, 11.7281]]    )
     elif "l16" in checkpoint_url:
          snake_case					=    torch.tensor([[41.5028, -22.8681, 45.6475]]    )
     elif "b4" in checkpoint_url:
          snake_case					=    torch.tensor([[-4.3868, 5.2932, -0.4137]]    )
     else:
          snake_case					=    torch.tensor([[-0.1792, -0.6465, 2.4263]]    )

     # verify logits
     assert torch.allclose(last_hidden_state[:, 0, :3]							,UpperCamelCase_							,atol=1e-4    )

     print(F'''Saving model to {pytorch_dump_folder_path}'''    )
     model.save_pretrained(UpperCamelCase_    )

     print(F'''Saving image processor to {pytorch_dump_folder_path}'''    )
     image_processor.save_pretrained(UpperCamelCase_    )


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE      :			List[Any]      				=   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--checkpoint_url",
     default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
     type=str,
     help="URL of the checkpoint you'd like to convert.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
 )

 _SCREAMING_SNAKE_CASE      :			Any      				=   parser.parse_args()
 convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




