


import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()






class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    lowercase       			= StableDiffusionLDMaDPipeline
    lowercase       			= TEXT_TO_IMAGE_PARAMS
    lowercase       			= TEXT_TO_IMAGE_BATCH_PARAMS
    lowercase       			= TEXT_TO_IMAGE_IMAGE_PARAMS

    def __lowercase       (       self   :  Union[str, Any]		)       ->  Any:
      torch.manual_seed(0		)
      lowerCAmelCase_      :				Union[str, 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_      :				int      =       DDIMScheduler(
          beta_start=0.00_085   ,						beta_end=0.012   ,						beta_schedule="""scaled_linear"""   ,						clip_sample=lowerCamelCase_   ,						set_alpha_to_one=lowerCamelCase_   ,						)
      torch.manual_seed(0		)
      lowerCAmelCase_      :				Dict      =       AutoencoderKL(
          block_out_channels=[32, 64]   ,						in_channels=6   ,						out_channels=6   ,						down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]   ,						up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]   ,						latent_channels=4   ,						)
      torch.manual_seed(0		)
      lowerCAmelCase_      :				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_      :				Dict      =       CLIPTextModel(lowerCamelCase_		)
      lowerCAmelCase_      :				Tuple      =       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

      lowerCAmelCase_      :				int      =       {
          """unet""": unet,
          """scheduler""": scheduler,
          """vae""": vae,
          """text_encoder""": text_encoder,
          """tokenizer""": tokenizer,
          """safety_checker""": None,
          """feature_extractor""": None,
      }
      return components

    def __lowercase       (       self   :  List[str]   ,						lowerCamelCase   :  Union[str, Any]   ,						lowerCamelCase   :  Dict=0		)       ->  Optional[int]:
      if str(lowerCamelCase_		).startswith("""mps"""		):
        lowerCAmelCase_      :				List[Any]      =       torch.manual_seed(lowerCamelCase_		)
      else:
        lowerCAmelCase_      :				Optional[int]      =       torch.Generator(device=lowerCamelCase_		).manual_seed(lowerCamelCase_		)
      lowerCAmelCase_      :				Tuple      =       {
          """prompt""": """A painting of a squirrel eating a burger""",
          """generator""": generator,
          """num_inference_steps""": 2,
          """guidance_scale""": 6.0,
          """output_type""": """numpy""",
      }
      return inputs

    def __lowercase       (       self   :  List[str]		)       ->  List[str]:
      lowerCAmelCase_      :				Optional[int]      =       """cpu"""  # ensure determinism for the device-dependent torch.Generator

      lowerCAmelCase_      :				int      =       self.get_dummy_components()
      lowerCAmelCase_      :				str      =       StableDiffusionLDMaDPipeline(**lowerCamelCase_		)
      lowerCAmelCase_      :				Union[str, Any]      =       ldmad_pipe.to(lowerCamelCase_		)
      ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase_		)

      lowerCAmelCase_      :				str      =       self.get_dummy_inputs(lowerCamelCase_		)
      lowerCAmelCase_      :				str      =       ldmad_pipe(**lowerCamelCase_		)
      lowerCAmelCase_,							lowerCAmelCase_      :				str      =       output.rgb, output.depth

      lowerCAmelCase_      :				Tuple      =       rgb[0, -3:, -3:, -1]
      lowerCAmelCase_      :				Any      =       depth[0, -3:, -1]

      assert rgb.shape == (1, 64, 64, 3)
      assert depth.shape == (1, 64, 64)

      lowerCAmelCase_      :				Optional[Any]      =       np.array(
          [0.37_338_176, 0.70_247, 0.74_203_193, 0.51_643_604, 0.58_256_793, 0.60_932_136, 0.4_181_095, 0.48_355_877, 0.46_535_262]		)
      lowerCAmelCase_      :				str      =       np.array([1_03.4_67_27, 85.812_004, 87.849_236]		)

      assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb		).max() < 1E-2
      assert np.abs(image_slice_depth.flatten() - expected_slice_depth		).max() < 1E-2

    def __lowercase       (       self   :  Optional[int]		)       ->  List[Any]:
      lowerCAmelCase_      :				List[Any]      =       self.get_dummy_components()
      lowerCAmelCase_      :				Optional[int]      =       StableDiffusionLDMaDPipeline(**lowerCamelCase_		)
      lowerCAmelCase_      :				Optional[Any]      =       ldmad_pipe.to(lowerCamelCase_		)
      ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase_		)

      lowerCAmelCase_      :				List[str]      =       self.get_dummy_inputs(lowerCamelCase_		)
      lowerCAmelCase_      :				Optional[Any]      =       3 * [inputs["""prompt"""]]

      # forward
      lowerCAmelCase_      :				Optional[int]      =       ldmad_pipe(**lowerCamelCase_		)
      lowerCAmelCase_,							lowerCAmelCase_      :				Optional[int]      =       output.rgb, output.depth
      lowerCAmelCase_      :				List[str]      =       rgb_slice_a[0, -3:, -3:, -1]
      lowerCAmelCase_      :				Optional[Any]      =       depth_slice_a[0, -3:, -1]

      lowerCAmelCase_      :				int      =       self.get_dummy_inputs(lowerCamelCase_		)
      lowerCAmelCase_      :				Optional[int]      =       3 * [inputs.pop("""prompt"""		)]

      lowerCAmelCase_      :				Any      =       ldmad_pipe.tokenizer(
          lowerCamelCase_   ,						padding="""max_length"""   ,						max_length=ldmad_pipe.tokenizer.model_max_length   ,						truncation=lowerCamelCase_   ,						return_tensors="""pt"""   ,						)
      lowerCAmelCase_      :				Union[str, Any]      =       text_inputs["""input_ids"""].to(lowerCamelCase_		)

      lowerCAmelCase_      :				List[Any]      =       ldmad_pipe.text_encoder(lowerCamelCase_		)[0]

      lowerCAmelCase_      :				List[Any]      =       prompt_embeds

      # forward
      lowerCAmelCase_      :				Any      =       ldmad_pipe(**lowerCamelCase_		)
      lowerCAmelCase_,							lowerCAmelCase_      :				Dict      =       output.rgb, output.depth
      lowerCAmelCase_      :				Tuple      =       rgb_slice_a[0, -3:, -3:, -1]
      lowerCAmelCase_      :				Union[str, Any]      =       depth_slice_a[0, -3:, -1]

      assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()		).max() < 1E-4
      assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()		).max() < 1E-4





    def __lowercase       (       self   :  str		)       ->  Tuple:
      lowerCAmelCase_      :				Tuple      =       """cpu"""  # ensure determinism for the device-dependent torch.Generator
      lowerCAmelCase_      :				Dict      =       self.get_dummy_components()
      lowerCAmelCase_      :				str      =       PNDMScheduler(skip_prk_steps=lowerCamelCase_		)
      lowerCAmelCase_      :				Dict      =       StableDiffusionLDMaDPipeline(**lowerCamelCase_		)
      lowerCAmelCase_      :				Any      =       ldmad_pipe.to(lowerCamelCase_		)
      ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase_		)

      lowerCAmelCase_      :				Optional[Any]      =       self.get_dummy_inputs(lowerCamelCase_		)
      lowerCAmelCase_      :				Any      =       """french fries"""
      lowerCAmelCase_      :				Union[str, Any]      =       ldmad_pipe(**lowerCamelCase_   ,						negative_prompt=lowerCamelCase_		)

      lowerCAmelCase_,							lowerCAmelCase_      :				Tuple      =       output.rgb, output.depth
      lowerCAmelCase_      :				int      =       rgb[0, -3:, -3:, -1]
      lowerCAmelCase_      :				Optional[int]      =       depth[0, -3:, -1]

      assert rgb.shape == (1, 64, 64, 3)
      assert depth.shape == (1, 64, 64)

      lowerCAmelCase_      :				str      =       np.array(
          [0.37_044, 0.71_811_503, 0.7_223_251, 0.48_603_675, 0.5_638_391, 0.6_364_948, 0.42_833_704, 0.4_901_315, 0.47_926_217]		)
      lowerCAmelCase_      :				List[Any]      =       np.array([1_07.8_47_38, 84.62_802, 89.962_135]		)
      assert np.abs(rgb_slice.flatten() - expected_slice_rgb		).max() < 1E-2
      assert np.abs(depth_slice.flatten() - expected_slice_depth		).max() < 1E-2







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







    """simple docstring"""



    def __lowercase       (       self   :  Any		)       ->  str:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()

    def __lowercase       (       self   :  Dict   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  List[str]="cpu"   ,						lowerCamelCase   :  Dict=torch.floataa   ,						lowerCamelCase   :  Union[str, Any]=0		)       ->  int:
      lowerCAmelCase_      :				Tuple      =       torch.Generator(device=lowerCamelCase_		).manual_seed(lowerCamelCase_		)
      lowerCAmelCase_      :				List[Any]      =       np.random.RandomState(lowerCamelCase_		).standard_normal((1, 4, 64, 64)		)
      lowerCAmelCase_      :				Union[str, Any]      =       torch.from_numpy(lowerCamelCase_		).to(device=lowerCamelCase_   ,						dtype=lowerCamelCase_		)
      lowerCAmelCase_      :				List[str]      =       {
          """prompt""": """a photograph of an astronaut riding a horse""",
          """latents""": latents,
          """generator""": generator,
          """num_inference_steps""": 3,
          """guidance_scale""": 7.5,
          """output_type""": """numpy""",
      }
      return inputs





    def __lowercase       (       self   :  int		)       ->  int:
      lowerCAmelCase_      :				List[str]      =       StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d"""		)
      lowerCAmelCase_      :				str      =       ldmad_pipe.to(lowerCamelCase_		)
      ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase_		)

      lowerCAmelCase_      :				Tuple      =       self.get_inputs(lowerCamelCase_		)
      lowerCAmelCase_      :				Tuple      =       ldmad_pipe(**lowerCamelCase_		)
      lowerCAmelCase_,							lowerCAmelCase_      :				List[str]      =       output.rgb, output.depth
      lowerCAmelCase_      :				List[str]      =       rgb[0, -3:, -3:, -1].flatten()
      lowerCAmelCase_      :				List[Any]      =       rgb[0, -3:, -1].flatten()

      assert rgb.shape == (1, 5_12, 5_12, 3)
      assert depth.shape == (1, 5_12, 5_12)

      lowerCAmelCase_      :				Optional[int]      =       np.array(
          [0.53_805_465, 0.56_707_305, 0.5_486_515, 0.57_012_236, 0.5_814_511, 0.56_253_487, 0.54_843_014, 0.55_092_263, 0.6_459_706]		)
      lowerCAmelCase_      :				Any      =       np.array(
          [0.9_263_781, 0.6_678_672, 0.5_486_515, 0.92_202_145, 0.67_831_135, 0.56_253_487, 0.9_241_694, 0.7_551_478, 0.6_459_706]		)
      assert np.abs(rgb_slice - expected_slice_rgb		).max() < 3E-3
      assert np.abs(depth_slice - expected_slice_depth		).max() < 3E-3







@nightly
@require_torch_gpu
class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    def __lowercase       (       self   :  Dict		)       ->  int:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()

    def __lowercase       (       self   :  Optional[Any]   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  Union[str, Any]="cpu"   ,						lowerCamelCase   :  Optional[Any]=torch.floataa   ,						lowerCamelCase   :  Union[str, Any]=0		)       ->  int:
      lowerCAmelCase_      :				str      =       torch.Generator(device=lowerCamelCase_		).manual_seed(lowerCamelCase_		)
      lowerCAmelCase_      :				str      =       np.random.RandomState(lowerCamelCase_		).standard_normal((1, 4, 64, 64)		)
      lowerCAmelCase_      :				List[Any]      =       torch.from_numpy(lowerCamelCase_		).to(device=lowerCamelCase_   ,						dtype=lowerCamelCase_		)
      lowerCAmelCase_      :				Any      =       {
          """prompt""": """a photograph of an astronaut riding a horse""",
          """latents""": latents,
          """generator""": generator,
          """num_inference_steps""": 50,
          """guidance_scale""": 7.5,
          """output_type""": """numpy""",
      }
      return inputs

    def __lowercase       (       self   :  List[Any]		)       ->  Dict:
      lowerCAmelCase_      :				List[str]      =       StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d"""		).to(lowerCamelCase_		)
      ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase_		)

      lowerCAmelCase_      :				str      =       self.get_inputs(lowerCamelCase_		)
      lowerCAmelCase_      :				Optional[Any]      =       ldmad_pipe(**lowerCamelCase_		)
      lowerCAmelCase_,							lowerCAmelCase_      :				Union[str, Any]      =       output.rgb, output.depth

      lowerCAmelCase_      :				str      =       0.495_586
      lowerCAmelCase_      :				List[str]      =       0.33_795_515
      lowerCAmelCase_      :				Dict      =       1_12.4_85_18
      lowerCAmelCase_      :				Dict      =       98.489_746
      assert np.abs(expected_rgb_mean - rgb.mean()		) < 1E-3
      assert np.abs(expected_rgb_std - rgb.std()		) < 1E-3
      assert np.abs(expected_depth_mean - depth.mean()		) < 1E-3
      assert np.abs(expected_depth_std - depth.std()		) < 1E-3





    def __lowercase       (       self   :  List[str]		)       ->  List[str]:
      lowerCAmelCase_      :				Optional[int]      =       StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d-4c"""		).to(lowerCamelCase_		)
      ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase_		)

      lowerCAmelCase_      :				Dict      =       self.get_inputs(lowerCamelCase_		)
      lowerCAmelCase_      :				str      =       ldmad_pipe(**lowerCamelCase_		)
      lowerCAmelCase_,							lowerCAmelCase_      :				List[str]      =       output.rgb, output.depth

      lowerCAmelCase_      :				int      =       0.4_194_127
      lowerCAmelCase_      :				Tuple      =       0.35_375_586
      lowerCAmelCase_      :				str      =       0.5_638_502
      lowerCAmelCase_      :				Tuple      =       0.34_686_103

      assert rgb.shape == (1, 5_12, 5_12, 3)
      assert depth.shape == (1, 5_12, 5_12, 1)
      assert np.abs(expected_rgb_mean - rgb.mean()		) < 1E-3
      assert np.abs(expected_rgb_std - rgb.std()		) < 1E-3
      assert np.abs(expected_depth_mean - depth.mean()		) < 1E-3
      assert np.abs(expected_depth_std - depth.std()		) < 1E-3







'''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 transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
__A :  List[str]    							=				logging.get_logger(__name__)







def     UpperCamelCase_      (   A__ :       Union[str, Any]							,   A__ :       Tuple=False ):
  '''simple docstring'''





  lowerCAmelCase_      :				Tuple      =       []
  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') )

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

  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[str]      =       [(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_      (   A__ :       Any							,   A__ :       Any							,   A__ :       Tuple=False ):
  '''simple docstring'''





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







def     UpperCamelCase_      (   A__ :       str ):
  '''simple docstring'''





  lowerCAmelCase_      :				Dict      =       ["""head.weight""", """head.bias"""]
  for k in ignore_keys:
    state_dict.pop(A__							,   A__ )







def     UpperCamelCase_      (   A__ :       List[Any]							,   A__ :       Optional[Any]							,   A__ :       Dict ):
  '''simple docstring'''





  lowerCAmelCase_      :				Tuple      =       dct.pop(A__ )
  lowerCAmelCase_      :				Tuple      =       val







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				str      =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
  lowerCAmelCase_      :				Optional[int]      =       Image.open(requests.get(A__							,   stream=A__ ).raw )
  return im







@torch.no_grad()
def     UpperCamelCase_      (   A__ :       Union[str, Any]							,   A__ :       List[Any] ):
  '''simple docstring'''





  lowerCAmelCase_      :				Optional[Any]      =       ViTConfig()
  lowerCAmelCase_      :				Any      =       False
  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  if vit_name[-5:] == "in21k":
    lowerCAmelCase_      :				int      =       True
    lowerCAmelCase_      :				Tuple      =       int(vit_name[-12:-10] )
    lowerCAmelCase_      :				Optional[int]      =       int(vit_name[-9:-6] )
  else:
    lowerCAmelCase_      :				Optional[int]      =       10_00
    lowerCAmelCase_      :				Tuple      =       """huggingface/label-files"""
    lowerCAmelCase_      :				Any      =       """imagenet-1k-id2label.json"""
    lowerCAmelCase_      :				Dict      =       json.load(open(hf_hub_download(A__							,   A__							,   repo_type="""dataset""" )							,   """r""" ) )
    lowerCAmelCase_      :				Union[str, Any]      =       {int(A__ ): v for k, v in idalabel.items()}
    lowerCAmelCase_      :				Union[str, Any]      =       idalabel
    lowerCAmelCase_      :				Union[str, Any]      =       {v: k for k, v in idalabel.items()}
    lowerCAmelCase_      :				Optional[int]      =       int(vit_name[-6:-4] )
    lowerCAmelCase_      :				Dict      =       int(vit_name[-3:] )
  # size of the architecture
  if "deit" in vit_name:
    if vit_name[9:].startswith("""tiny""" ):
      lowerCAmelCase_      :				int      =       1_92
      lowerCAmelCase_      :				List[str]      =       7_68
      lowerCAmelCase_      :				List[str]      =       12
      lowerCAmelCase_      :				int      =       3
    elif vit_name[9:].startswith("""small""" ):
      lowerCAmelCase_      :				Optional[Any]      =       3_84
      lowerCAmelCase_      :				Optional[int]      =       15_36
      lowerCAmelCase_      :				Dict      =       12
      lowerCAmelCase_      :				str      =       6
    else:
      pass
  else:
    if vit_name[4:].startswith("""small""" ):
      lowerCAmelCase_      :				Tuple      =       7_68
      lowerCAmelCase_      :				Any      =       23_04
      lowerCAmelCase_      :				List[str]      =       8
      lowerCAmelCase_      :				List[str]      =       8
    elif vit_name[4:].startswith("""base""" ):
      pass
    elif vit_name[4:].startswith("""large""" ):
      lowerCAmelCase_      :				Dict      =       10_24
      lowerCAmelCase_      :				List[Any]      =       40_96
      lowerCAmelCase_      :				Any      =       24
      lowerCAmelCase_      :				List[str]      =       16
    elif vit_name[4:].startswith("""huge""" ):
      lowerCAmelCase_      :				Optional[int]      =       12_80
      lowerCAmelCase_      :				Dict      =       51_20
      lowerCAmelCase_      :				Union[str, Any]      =       32
      lowerCAmelCase_      :				Optional[int]      =       16

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

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

  # load HuggingFace model
  if vit_name[-5:] == "in21k":
    lowerCAmelCase_      :				int      =       ViTModel(A__ ).eval()
  else:
    lowerCAmelCase_      :				Optional[int]      =       ViTForImageClassification(A__ ).eval()
  model.load_state_dict(A__ )

  # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
  if "deit" in vit_name:
    lowerCAmelCase_      :				Any      =       DeiTImageProcessor(size=config.image_size )
  else:
    lowerCAmelCase_      :				Any      =       ViTImageProcessor(size=config.image_size )
  lowerCAmelCase_      :				Tuple      =       image_processor(images=prepare_img()							,   return_tensors="""pt""" )
  lowerCAmelCase_      :				int      =       encoding["""pixel_values"""]
  lowerCAmelCase_      :				int      =       model(A__ )

  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_      :				Union[str, Any]      =       timm_model(A__ )
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(A__							,   outputs.logits							,   atol=1E-3 )

  Path(A__ ).mkdir(exist_ok=A__ )
  print(f'Saving model {vit_name} to {pytorch_dump_folder_path}' )
  model.save_pretrained(A__ )
  print(f'Saving image processor to {pytorch_dump_folder_path}' )
  image_processor.save_pretrained(A__ )


if __name__ == "__main__":
  __A :  Optional[int]    							=				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--vit_name",
      default="vit_base_patch16_224",
      type=str,
      help="Name of the 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."
  )

  __A :  Union[str, Any]    							=				parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)



