





'''simple docstring'''




import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)

enable_full_determinism()
class 			_lowerCAmelCase		(			A__							,     A__							,     unittest.TestCase	):






     """simple docstring"""






     snake_case_						      =   UNetaDModel
     snake_case_						      =   "sample"



     @property
     def      lowerCAmelCase			(   self							:			Tuple						)->	Tuple:
        snake_case 						=   4
        snake_case 						=   3
        snake_case 						=   (32, 32)

        snake_case 						=   floats_tensor((batch_size, num_channels) + sizes						).to(__snake_case						)
        snake_case 						=   torch.tensor([10]						).to(__snake_case						)

        return {"sample": noise, "timestep": time_step}



     @property
     def      lowerCAmelCase			(   self							:			str						)->	List[str]:
        return (3, 32, 32)



     @property
     def      lowerCAmelCase			(   self							:			Optional[int]						)->	List[str]:
        return (3, 32, 32)



     def      lowerCAmelCase			(   self							:			Tuple						)->	List[str]:
        snake_case 						=   {
            """block_out_channels""": (32, 64),
            """down_block_types""": ("""DownBlock2D""", """AttnDownBlock2D"""),
            """up_block_types""": ("""AttnUpBlock2D""", """UpBlock2D"""),
            """attention_head_dim""": 3,
            """out_channels""": 3,
            """in_channels""": 3,
            """layers_per_block""": 2,
            """sample_size""": 32,
        }
        snake_case 						=   self.dummy_input
        return init_dict, inputs_dict




class 			_lowerCAmelCase		(			A__							,     A__							,     unittest.TestCase	):






     """simple docstring"""






     snake_case_						      =   UNetaDModel
     snake_case_						      =   "sample"



     @property
     def      lowerCAmelCase			(   self							:			Dict						)->	int:
        snake_case 						=   4
        snake_case 						=   4
        snake_case 						=   (32, 32)

        snake_case 						=   floats_tensor((batch_size, num_channels) + sizes						).to(__snake_case						)
        snake_case 						=   torch.tensor([10]						).to(__snake_case						)

        return {"sample": noise, "timestep": time_step}



     @property
     def      lowerCAmelCase			(   self							:			str						)->	Any:
        return (4, 32, 32)



     @property
     def      lowerCAmelCase			(   self							:			str						)->	str:
        return (4, 32, 32)



     def      lowerCAmelCase			(   self							:			Dict						)->	str:
        snake_case 						=   {
            """sample_size""": 32,
            """in_channels""": 4,
            """out_channels""": 4,
            """layers_per_block""": 2,
            """block_out_channels""": (32, 64),
            """attention_head_dim""": 32,
            """down_block_types""": ("""DownBlock2D""", """DownBlock2D"""),
            """up_block_types""": ("""UpBlock2D""", """UpBlock2D"""),
        }
        snake_case 						=   self.dummy_input
        return init_dict, inputs_dict



     def      lowerCAmelCase			(   self							:			List[str]						)->	Tuple:
        snake_case						,    snake_case 						=   UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""   ,       output_loading_info=__snake_case						)

        self.assertIsNotNone(__snake_case						)
        self.assertEqual(len(loading_info["""missing_keys"""]						)   ,       0						)

        model.to(__snake_case						)
        snake_case 						=   model(**self.dummy_input						).sample

        assert image is not None, "Make sure output is not None"



     @unittest.skipIf(torch_device != """cuda"""   ,       """This test is supposed to run on GPU"""						)
     def      lowerCAmelCase			(   self							:			List[str]						)->	Optional[int]:
        snake_case						,    snake_case 						=   UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""   ,       output_loading_info=__snake_case						)
        model.to(__snake_case						)
        snake_case 						=   model(**self.dummy_input						).sample

        assert image is not None, "Make sure output is not None"



     @unittest.skipIf(torch_device != """cuda"""   ,       """This test is supposed to run on GPU"""						)
     def      lowerCAmelCase			(   self							:			List[Any]						)->	Union[str, Any]:
        # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
        snake_case						,    snake_case 						=   UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""   ,       output_loading_info=__snake_case						)
        model_accelerate.to(__snake_case						)
        model_accelerate.eval()

        snake_case 						=   torch.randn(
            1   ,       model_accelerate.config.in_channels   ,       model_accelerate.config.sample_size   ,       model_accelerate.config.sample_size   ,       generator=torch.manual_seed(0						)   ,       )
        snake_case 						=   noise.to(__snake_case						)
        snake_case 						=   torch.tensor([10] * noise.shape[0]						).to(__snake_case						)

        snake_case 						=   model_accelerate(__snake_case   ,       __snake_case						)["""sample"""]

        # two models don't need to stay in the device at the same time
        del model_accelerate
        torch.cuda.empty_cache()
        gc.collect()

        snake_case						,    snake_case 						=   UNetaDModel.from_pretrained(
            """fusing/unet-ldm-dummy-update"""   ,       output_loading_info=__snake_case   ,       low_cpu_mem_usage=__snake_case						)
        model_normal_load.to(__snake_case						)
        model_normal_load.eval()
        snake_case 						=   model_normal_load(__snake_case   ,       __snake_case						)["""sample"""]

        assert torch_all_close(__snake_case   ,       __snake_case   ,       rtol=1e-3						)



     def      lowerCAmelCase			(   self							:			Any						)->	Any:
        snake_case 						=   UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""						)
        model.eval()
        model.to(__snake_case						)

        snake_case 						=   torch.randn(
            1   ,       model.config.in_channels   ,       model.config.sample_size   ,       model.config.sample_size   ,       generator=torch.manual_seed(0						)   ,       )
        snake_case 						=   noise.to(__snake_case						)
        snake_case 						=   torch.tensor([10] * noise.shape[0]						).to(__snake_case						)

        with torch.no_grad():
           snake_case 						=   model(__snake_case   ,       __snake_case						).sample

        snake_case 						=   output[0, -1, -3:, -3:].flatten().cpu()
        # fmt: off
        snake_case 						=   torch.tensor([-13.32_58, -20.11_00, -15.98_73, -17.66_17, -23.05_96, -17.94_19, -13.36_75, -16.18_89, -12.38_00]						)
        # fmt: on

        self.assertTrue(torch_all_close(__snake_case   ,       __snake_case   ,       rtol=1e-3						)						)




class 			_lowerCAmelCase		(			A__							,     A__							,     unittest.TestCase	):






     """simple docstring"""






     snake_case_						      =   UNetaDModel
     snake_case_						      =   "sample"



     @property
     def      lowerCAmelCase			(   self							:			Optional[int]   ,       __snake_case							:			str=(32, 32)						)->	List[Any]:
        snake_case 						=   4
        snake_case 						=   3

        snake_case 						=   floats_tensor((batch_size, num_channels) + sizes						).to(__snake_case						)
        snake_case 						=   torch.tensor(batch_size * [10]						).to(dtype=torch.intaa   ,       device=__snake_case						)

        return {"sample": noise, "timestep": time_step}



     @property
     def      lowerCAmelCase			(   self							:			Optional[int]						)->	Union[str, Any]:
        return (3, 32, 32)



     @property
     def      lowerCAmelCase			(   self							:			List[str]						)->	Optional[Any]:
        return (3, 32, 32)



     def      lowerCAmelCase			(   self							:			Optional[int]						)->	List[Any]:
        snake_case 						=   {
            """block_out_channels""": [32, 64, 64, 64],
            """in_channels""": 3,
            """layers_per_block""": 1,
            """out_channels""": 3,
            """time_embedding_type""": """fourier""",
            """norm_eps""": 1e-6,
            """mid_block_scale_factor""": math.sqrt(2.0						),
            """norm_num_groups""": None,
            """down_block_types""": [
                """SkipDownBlock2D""",
                """AttnSkipDownBlock2D""",
                """SkipDownBlock2D""",
                """SkipDownBlock2D""",
            ],
            """up_block_types""": [
                """SkipUpBlock2D""",
                """SkipUpBlock2D""",
                """AttnSkipUpBlock2D""",
                """SkipUpBlock2D""",
            ],
        }
        snake_case 						=   self.dummy_input
        return init_dict, inputs_dict



     @slow
     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	List[Any]:
        snake_case						,    snake_case 						=   UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""   ,       output_loading_info=__snake_case						)
        self.assertIsNotNone(__snake_case						)
        self.assertEqual(len(loading_info["""missing_keys"""]						)   ,       0						)

        model.to(__snake_case						)
        snake_case 						=   self.dummy_input
        snake_case 						=   floats_tensor((4, 3) + (2_56, 2_56)						).to(__snake_case						)
        snake_case 						=   noise
        snake_case 						=   model(**__snake_case						)

        assert image is not None, "Make sure output is not None"



     @slow
     def      lowerCAmelCase			(   self							:			Any						)->	Union[str, Any]:
        snake_case 						=   UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""						)
        model.to(__snake_case						)

        snake_case 						=   4
        snake_case 						=   3
        snake_case 						=   (2_56, 2_56)

        snake_case 						=   torch.ones((batch_size, num_channels) + sizes						).to(__snake_case						)
        snake_case 						=   torch.tensor(batch_size * [1e-4]						).to(__snake_case						)

        with torch.no_grad():
           snake_case 						=   model(__snake_case   ,       __snake_case						).sample

        snake_case 						=   output[0, -3:, -3:, -1].flatten().cpu()
        # fmt: off
        snake_case 						=   torch.tensor([-48_42.86_91, -64_99.66_31, -38_00.19_53, -79_78.26_86, -1_09_80.71_29, -2_00_28.85_35, 81_48.28_22, 23_42.29_05, 5_67.76_08]						)
        # fmt: on

        self.assertTrue(torch_all_close(__snake_case   ,       __snake_case   ,       rtol=1e-2						)						)



     def      lowerCAmelCase			(   self							:			Optional[Any]						)->	Optional[Any]:
        snake_case 						=   UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""						)
        model.to(__snake_case						)

        snake_case 						=   4
        snake_case 						=   3
        snake_case 						=   (32, 32)

        snake_case 						=   torch.ones((batch_size, num_channels) + sizes						).to(__snake_case						)
        snake_case 						=   torch.tensor(batch_size * [1e-4]						).to(__snake_case						)

        with torch.no_grad():
           snake_case 						=   model(__snake_case   ,       __snake_case						).sample

        snake_case 						=   output[0, -3:, -3:, -1].flatten().cpu()
        # fmt: off
        snake_case 						=   torch.tensor([-0.03_25, -0.09_00, -0.08_69, -0.03_32, -0.07_25, -0.02_70, -0.01_01, 0.02_27, 0.02_56]						)
        # fmt: on

        self.assertTrue(torch_all_close(__snake_case   ,       __snake_case   ,       rtol=1e-2						)						)



     def      lowerCAmelCase			(   self							:			Any						)->	Optional[Any]:
        # not required for this model
        pass








'''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()
_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Optional[int]      ,    __lowerCAmelCase       :							List[Any]=False		)		->  List[str]:
   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'''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"
      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 	__lowerCamelCase			(      __lowerCAmelCase       :							Dict      ,    __lowerCAmelCase       :							Optional[Any]      ,    __lowerCAmelCase       :							int=False		)		->  Union[str, Any]:
   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'''blocks.{i}.attn.qkv.weight'''		)
      snake_case 						=   state_dict.pop(F'''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 	__lowerCamelCase			(      __lowerCAmelCase       :							List[str]		)		->  List[str]:
   snake_case 						=   ["""head.weight""", """head.bias"""]
   for k in ignore_keys:
      state_dict.pop(__lowerCAmelCase      ,    __lowerCAmelCase		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Optional[Any]      ,    __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							Tuple		)		->  int:
   snake_case 						=   dct.pop(__lowerCAmelCase		)
   snake_case 						=   val





def 	__lowerCamelCase			(      )		->  Optional[Any]:
   snake_case 						=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
   snake_case 						=   Image.open(requests.get(__lowerCAmelCase      ,    stream=__lowerCAmelCase		).raw		)
   return im





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

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

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

   # load HuggingFace model
   if vit_name[-5:] == "in21k":
      snake_case 						=   ViTModel(__lowerCAmelCase		).eval()
   else:
      snake_case 						=   ViTForImageClassification(__lowerCAmelCase		).eval()
   model.load_state_dict(__lowerCAmelCase		)

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

   if base_model:
      snake_case 						=   timm_model.forward_features(__lowerCAmelCase		)
      assert timm_pooled_output.shape == outputs.pooler_output.shape
      assert torch.allclose(__lowerCAmelCase      ,    outputs.pooler_output      ,    atol=1e-3		)
   else:
      snake_case 						=   timm_model(__lowerCAmelCase		)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(__lowerCAmelCase      ,    outputs.logits      ,    atol=1e-3		)

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


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE							     =			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."
 )

 _SCREAMING_SNAKE_CASE							     =			parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)

