




import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase      :						Optional[Any]							=  logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_=False		)  ->			Optional[int]:



       """simple docstring"""

       A__ 					=       []

       # 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"
              A__ 					=       [(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 SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_=False		)  ->			Dict:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			Any:



       """simple docstring"""

       A__ 					=       ['''head.weight''', '''head.bias''']
       for k in ignore_keys:
              state_dict.pop(lowercase_					,							lowercase_		)







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_		)  ->			str:



       """simple docstring"""

       A__ 					=       dct.pop(lowercase_		)
       A__ 					=       val







def SCREAMING_SNAKE_CASE							(			)  ->			Dict:



       """simple docstring"""

       A__ 					=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
       A__ 					=       Image.open(requests.get(lowercase_					,							stream=lowercase_		).raw		)
       return im







@torch.no_grad()
def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_=False		)  ->			List[Any]:



       """simple docstring"""

       A__ 					=       BitConfig(
           global_padding='''same'''					,							layer_type='''bottleneck'''					,							depths=(3, 4, 9)					,							out_features=['''stage3''']					,							embedding_dynamic_padding=lowercase_					,							)
       A__ 					=       ViTHybridConfig(backbone_config=lowercase_					,							image_size=384					,							num_labels=1_000		)
       A__ 					=       False

       # load original model from timm
       A__ 					=       timm.create_model(lowercase_					,							pretrained=lowercase_		)
       timm_model.eval()

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

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

       # load HuggingFace model
       if vit_name[-5:] == "in21k":
              A__ 					=       ViTHybridModel(lowercase_		).eval()
       else:
              A__ 					=       ViTHybridForImageClassification(lowercase_		).eval()
       model.load_state_dict(lowercase_		)

       # create image processor
       A__ 					=       create_transform(**resolve_data_config({}					,							model=lowercase_		)		)
       A__ 					=       transform.transforms

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

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

       A__ 					=       prepare_img()
       A__ 					=       transform(lowercase_		).unsqueeze(0		)
       A__ 					=       processor(lowercase_					,							return_tensors='''pt'''		).pixel_values

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

       # verify logits
       with torch.no_grad():
              A__ 					=       model(lowercase_		)
              A__ 					=       outputs.logits

       print('''Predicted class:'''					,							logits.argmax(-1		).item()		)
       if base_model:
              A__ 					=       timm_model.forward_features(lowercase_		)
              assert timm_pooled_output.shape == outputs.pooler_output.shape
              assert torch.allclose(lowercase_					,							outputs.pooler_output					,							atol=1E-3		)
       else:
              A__ 					=       timm_model(lowercase_		)
              assert timm_logits.shape == outputs.logits.shape
              assert torch.allclose(lowercase_					,							outputs.logits					,							atol=1E-3		)
       print('''Looks ok!'''		)

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

       if push_to_hub:
              print(f"""Pushing model and processor to the hub {vit_name}"""		)
              model.push_to_hub(f"""ybelkada/{vit_name}"""		)
              processor.push_to_hub(f"""ybelkada/{vit_name}"""		)


if __name__ == "__main__":
  _lowerCamelCase      :						Dict							=  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--vit_name""",
      default="""vit_base_r50_s16_384""",
      type=str,
      help="""Name of the hybrid ViT timm model you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )
  parser.add_argument(
      """--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
  )

  _lowerCamelCase      :						Dict							=  parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)









import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     @property
     def        lowerCAmelCase__		(    self							):
         torch.manual_seed(0							)
         _A     =							UNetaDModel(
             block_out_channels=(32, 64)				,     layers_per_block=2				,     sample_size=32				,     in_channels=3				,     out_channels=3				,     down_block_types=('DownBlock2D', 'AttnDownBlock2D')				,     up_block_types=('AttnUpBlock2D', 'UpBlock2D')				,     )
         return model




     def        lowerCAmelCase__		(    self							):
         _A     =							self.dummy_uncond_unet
         _A     =							KarrasVeScheduler()

         _A     =							KarrasVePipeline(unet=snake_case_				,     scheduler=snake_case_							)
         pipe.to(snake_case_							)
         pipe.set_progress_bar_config(disable=snake_case_							)

         _A     =							torch.manual_seed(0							)
         _A     =							pipe(num_inference_steps=2				,     generator=snake_case_				,     output_type='numpy'							).images

         _A     =							torch.manual_seed(0							)
         _A     =							pipe(num_inference_steps=2				,     generator=snake_case_				,     output_type='numpy'				,     return_dict=snake_case_							)[0]

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

         assert image.shape == (1, 32, 32, 3)
         _A     =							np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

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







@slow
@require_torch
class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     def        lowerCAmelCase__		(    self							):
         _A     =							'google/ncsnpp-celebahq-256'
         _A     =							UNetaDModel.from_pretrained(snake_case_							)
         _A     =							KarrasVeScheduler()

         _A     =							KarrasVePipeline(unet=snake_case_				,     scheduler=snake_case_							)
         pipe.to(snake_case_							)
         pipe.set_progress_bar_config(disable=snake_case_							)

         _A     =							torch.manual_seed(0							)
         _A     =							pipe(num_inference_steps=20				,     generator=snake_case_				,     output_type='numpy'							).images

         _A     =							image[0, -3:, -3:, -1]
         assert image.shape == (1, 256, 256, 3)
         _A     =							np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]							)

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





