




import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Optional[Any]    ,    __UpperCAmelCase		:			Tuple=False    ,    __UpperCAmelCase		:			str=False    ,    __UpperCAmelCase		:			List[Any]="data2vec."							)      ->				Optional[int]:
      SCREAMING_SNAKE_CASE_					=    'backbone.' if is_semantic else ''

      SCREAMING_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"{prefix}blocks.{i}.norm1.weight", f"{hf_prefix}encoder.layer.{i}.layernorm_before.weight")							)
            rename_keys.append((f"{prefix}blocks.{i}.norm1.bias", f"{hf_prefix}encoder.layer.{i}.layernorm_before.bias")							)
            rename_keys.append(
                (f"{prefix}blocks.{i}.attn.proj.weight", f"{hf_prefix}encoder.layer.{i}.attention.output.dense.weight")							)
            rename_keys.append(
                (f"{prefix}blocks.{i}.attn.proj.bias", f"{hf_prefix}encoder.layer.{i}.attention.output.dense.bias")							)
            rename_keys.append(
                (f"{prefix}blocks.{i}.norm2.weight", f"{hf_prefix}encoder.layer.{i}.layernorm_after.weight")							)
            rename_keys.append((f"{prefix}blocks.{i}.norm2.bias", f"{hf_prefix}encoder.layer.{i}.layernorm_after.bias")							)
            rename_keys.append(
                (f"{prefix}blocks.{i}.mlp.fc1.weight", f"{hf_prefix}encoder.layer.{i}.intermediate.dense.weight")							)
            rename_keys.append(
                (f"{prefix}blocks.{i}.mlp.fc1.bias", f"{hf_prefix}encoder.layer.{i}.intermediate.dense.bias")							)
            rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.weight", f"{hf_prefix}encoder.layer.{i}.output.dense.weight")							)
            rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.bias", f"{hf_prefix}encoder.layer.{i}.output.dense.bias")							)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              (f"{prefix}cls_token", f"{hf_prefix}embeddings.cls_token"),
              (f"{prefix}patch_embed.proj.weight", f"{hf_prefix}embeddings.patch_embeddings.projection.weight"),
              (f"{prefix}patch_embed.proj.bias", f"{hf_prefix}embeddings.patch_embeddings.projection.bias"),
          ]							)

      if has_lm_head:
            # mask token + shared relative position bias + layernorm
            rename_keys.extend(
                [
                    ('mask_token', f"{hf_prefix}embeddings.mask_token"),
                    (
                        'rel_pos_bias.relative_position_bias_table',
                        f"{hf_prefix}encoder.relative_position_bias.relative_position_bias_table",
                    ),
                    (
                        'rel_pos_bias.relative_position_index',
                        f"{hf_prefix}encoder.relative_position_bias.relative_position_index",
                    ),
                    ('norm.weight', 'layernorm.weight'),
                    ('norm.bias', 'layernorm.bias'),
                ]							)
      elif is_semantic:
            # semantic segmentation classification heads
            rename_keys.extend(
                [
                    ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
                    ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
                    ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
                    ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
                ]							)
      else:
            # layernorm + classification head
            rename_keys.extend(
                [
                    ('fc_norm.weight', f"{hf_prefix}pooler.layernorm.weight"),
                    ('fc_norm.bias', f"{hf_prefix}pooler.layernorm.bias"),
                    ('head.weight', 'classifier.weight'),
                    ('head.bias', 'classifier.bias'),
                ]							)

      return rename_keys

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Tuple    ,    __UpperCAmelCase		:			Optional[Any]    ,    __UpperCAmelCase		:			Any=False    ,    __UpperCAmelCase		:			Optional[Any]=False    ,    __UpperCAmelCase		:			Union[str, Any]="data2vec_vision."							)      ->				List[str]:
      for i in range(config.num_hidden_layers							):
            SCREAMING_SNAKE_CASE_					=    'backbone.' if is_semantic else ''
            # queries, keys and values
            SCREAMING_SNAKE_CASE_					=    state_dict.pop(f"{prefix}blocks.{i}.attn.qkv.weight"							)
            SCREAMING_SNAKE_CASE_					=    state_dict.pop(f"{prefix}blocks.{i}.attn.q_bias"							)
            SCREAMING_SNAKE_CASE_					=    state_dict.pop(f"{prefix}blocks.{i}.attn.v_bias"							)

            SCREAMING_SNAKE_CASE_					=    in_proj_weight[
                : config.hidden_size, :
            ]
            SCREAMING_SNAKE_CASE_					=    q_bias
            SCREAMING_SNAKE_CASE_					=    in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            SCREAMING_SNAKE_CASE_					=    in_proj_weight[
                -config.hidden_size :, :
            ]
            SCREAMING_SNAKE_CASE_					=    v_bias

            # gamma_1 and gamma_2
            # we call them lambda because otherwise they are renamed when using .from_pretrained
            SCREAMING_SNAKE_CASE_					=    state_dict.pop(f"{prefix}blocks.{i}.gamma_1"							)
            SCREAMING_SNAKE_CASE_					=    state_dict.pop(f"{prefix}blocks.{i}.gamma_2"							)

            SCREAMING_SNAKE_CASE_					=    gamma_a
            SCREAMING_SNAKE_CASE_					=    gamma_a

            # relative_position bias table + index
            if not has_lm_head:
                  # each layer has its own relative position bias
                  SCREAMING_SNAKE_CASE_					=    state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_bias_table"							)
                  SCREAMING_SNAKE_CASE_					=    state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_index"							)

                  SCREAMING_SNAKE_CASE_					=    table
                  SCREAMING_SNAKE_CASE_					=    index

def       UpperCAmelCase_							(   )      ->				Tuple:
      SCREAMING_SNAKE_CASE_					=    argparse.ArgumentParser(
          'Convert Data2VecVision to HF for image classification and pretraining'    ,    add_help=__UpperCAmelCase							)
      parser.add_argument('--hf_checkpoint_name'    ,    type=__UpperCAmelCase							)
      parser.add_argument('--input_size'    ,    default=2_24    ,    type=__UpperCAmelCase    ,    help='images input size'							)
      parser.add_argument('--beit_checkpoint'    ,    default=''    ,    help='beit checkpoint'							)

      return parser.parse_args()

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Optional[Any]    ,    __UpperCAmelCase		:			Tuple    ,    __UpperCAmelCase		:			Union[str, Any]							)      ->				Dict:
      def load_state_dict(__UpperCAmelCase		:			Optional[Any]    ,    __UpperCAmelCase		:			int    ,    __UpperCAmelCase		:			Any=""    ,    __UpperCAmelCase		:			Dict="relative_position_index"							):
            SCREAMING_SNAKE_CASE_					=    []
            SCREAMING_SNAKE_CASE_					=    []
            SCREAMING_SNAKE_CASE_					=    []
            # copy state_dict so _load_from_state_dict can modify it
            SCREAMING_SNAKE_CASE_					=    getattr(__UpperCAmelCase    ,    '_metadata'    ,    __UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_					=    state_dict.copy()
            if metadata is not None:
                  SCREAMING_SNAKE_CASE_					=    metadata

            def load(__UpperCAmelCase		:			Any    ,    __UpperCAmelCase		:			Union[str, Any]=""							):
                  SCREAMING_SNAKE_CASE_					=    {} if metadata is None else metadata.get(prefix[:-1]    ,    {}							)
                  module._load_from_state_dict(
                      __UpperCAmelCase    ,    __UpperCAmelCase    ,    __UpperCAmelCase    ,    __UpperCAmelCase    ,    __UpperCAmelCase    ,    __UpperCAmelCase    ,    __UpperCAmelCase							)
                  for name, child in module._modules.items():
                        if child is not None:
                              load(__UpperCAmelCase    ,    prefix + name + '.'							)

            load(__UpperCAmelCase    ,    prefix=__UpperCAmelCase							)

            SCREAMING_SNAKE_CASE_					=    []
            SCREAMING_SNAKE_CASE_					=    []
            for key in missing_keys:
                  SCREAMING_SNAKE_CASE_					=    True
                  for ignore_key in ignore_missing.split('|'							):
                        if ignore_key in key:
                              SCREAMING_SNAKE_CASE_					=    False
                              break
                  if keep_flag:
                        warn_missing_keys.append(__UpperCAmelCase							)
                  else:
                        ignore_missing_keys.append(__UpperCAmelCase							)

            SCREAMING_SNAKE_CASE_					=    warn_missing_keys

            if len(__UpperCAmelCase							) > 0:
                  print(
                      'Weights of {} not initialized from pretrained model: {}'.format(
                          model.__class__.__name__    ,    __UpperCAmelCase							)							)
            if len(__UpperCAmelCase							) > 0:
                  print('Weights from pretrained model not used in {}: {}'.format(model.__class__.__name__    ,    __UpperCAmelCase							)							)
            if len(__UpperCAmelCase							) > 0:
                  print(
                      'Ignored weights of {} not initialized from pretrained model: {}'.format(
                          model.__class__.__name__    ,    __UpperCAmelCase							)							)
            if len(__UpperCAmelCase							) > 0:
                  print('\n'.join(__UpperCAmelCase							)							)

      SCREAMING_SNAKE_CASE_					=    {
          'pretrained': False,
          'use_shared_rel_pos_bias': True,
          'use_abs_pos_emb': False,
          'init_values': 0.1,
      }

      if is_finetuned:
            model_kwargs.update(
                {
                    'num_classes': 10_00,
                    'use_mean_pooling': True,
                    'init_scale': 0.0_0_1,
                    'use_rel_pos_bias': True,
                }							)

      SCREAMING_SNAKE_CASE_					=    create_model(
          'beit_large_patch16_224' if is_large else 'beit_base_patch16_224'    ,    **__UpperCAmelCase    ,    )
      SCREAMING_SNAKE_CASE_					=    model.patch_embed.patch_size
      SCREAMING_SNAKE_CASE_					=    (args.input_size // patch_size[0], args.input_size // patch_size[1])
      SCREAMING_SNAKE_CASE_					=    torch.load(args.beit_checkpoint    ,    map_location='cpu'							)

      print(f"Load ckpt from {args.beit_checkpoint}"							)
      SCREAMING_SNAKE_CASE_					=    None
      for model_key in ("model", "module"):
            if model_key in checkpoint:
                  SCREAMING_SNAKE_CASE_					=    checkpoint[model_key]
                  print(f"Load state_dict by model_key = {model_key}"							)
                  break

      SCREAMING_SNAKE_CASE_					=    list(checkpoint_model.keys()							)
      for key in all_keys:
            if "relative_position_index" in key:
                  checkpoint_model.pop(__UpperCAmelCase							)

            if "relative_position_bias_table" in key:
                  SCREAMING_SNAKE_CASE_					=    checkpoint_model[key]
                  SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    rel_pos_bias.size()
                  SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    model.state_dict()[key].size()
                  SCREAMING_SNAKE_CASE_					=    model.patch_embed.patch_shape
                  if dst_patch_shape[0] != dst_patch_shape[1]:
                        raise NotImplementedError()

      load_state_dict(__UpperCAmelCase    ,    __UpperCAmelCase    ,    prefix=''							)

      return model

def       UpperCAmelCase_							(   )      ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE_					=    get_args()

      SCREAMING_SNAKE_CASE_					=    'ft1k' in args.hf_checkpoint_name
      SCREAMING_SNAKE_CASE_					=    'large' in args.hf_checkpoint_name

      if is_finetuned:
            # To convert Beit's data2vec_vision to HF you need to copy
            # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
            # into this folder.
            import modeling_finetune  # noqa: F401
      else:
            # To convert Beit's data2vec_vision to HF you need to copy
            # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
            # into this folder
            # IMPORTANT: Note that for now we've only converted the down-stream
            # model and not the full pretrained model. This means for the integration
            # test you need to add a `return x` after the following line:
            # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
            # to make the integration test pass.
            import modeling_cyclical  # noqa: F401

      # 1. Create model config
      SCREAMING_SNAKE_CASE_					=    DataaVecVisionConfig()
      if is_finetuned:
            SCREAMING_SNAKE_CASE_					=    True
            SCREAMING_SNAKE_CASE_					=    False
            SCREAMING_SNAKE_CASE_					=    True
            SCREAMING_SNAKE_CASE_					=    10_00

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

      if is_large:
            SCREAMING_SNAKE_CASE_					=    10_24
            SCREAMING_SNAKE_CASE_					=    40_96
            SCREAMING_SNAKE_CASE_					=    24
            SCREAMING_SNAKE_CASE_					=    16

      # 2. Load Beit model
      SCREAMING_SNAKE_CASE_					=    load_beit_model(__UpperCAmelCase    ,    __UpperCAmelCase    ,    __UpperCAmelCase							)
      orig_model.eval()

      # 3. Forward Beit model
      SCREAMING_SNAKE_CASE_					=    BeitImageProcessor(size=config.image_size    ,    do_center_crop=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_					=    Image.open('../../../../tests/fixtures/tests_samples/COCO/000000039769.png'							)
      SCREAMING_SNAKE_CASE_					=    image_processor(images=__UpperCAmelCase    ,    return_tensors='pt'							)
      SCREAMING_SNAKE_CASE_					=    encoding['pixel_values']

      SCREAMING_SNAKE_CASE_					=    (pixel_values,) if is_finetuned else (pixel_values, None)
      with torch.no_grad():
            SCREAMING_SNAKE_CASE_					=    orig_model(*__UpperCAmelCase							)

      # 4. Load HF Data2VecVision model
      if is_finetuned:
            SCREAMING_SNAKE_CASE_					=    DataaVecVisionForImageClassification(__UpperCAmelCase							)
            hf_model.eval()
            SCREAMING_SNAKE_CASE_					=    False
            SCREAMING_SNAKE_CASE_					=    'data2vec_vision.'
      else:
            SCREAMING_SNAKE_CASE_					=    DataaVecVisionModel(__UpperCAmelCase							)
            hf_model.eval()
            SCREAMING_SNAKE_CASE_					=    True
            SCREAMING_SNAKE_CASE_					=    ''

      SCREAMING_SNAKE_CASE_					=    create_rename_keys(__UpperCAmelCase    ,    hf_prefix=__UpperCAmelCase    ,    has_lm_head=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_					=    orig_model.state_dict()
      for src, dest in rename_keys:
            SCREAMING_SNAKE_CASE_					=    state_dict.pop(__UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_					=    val

      read_in_q_k_v(__UpperCAmelCase    ,    __UpperCAmelCase    ,    hf_prefix=__UpperCAmelCase    ,    has_lm_head=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    hf_model.load_state_dict(__UpperCAmelCase    ,    strict=__UpperCAmelCase							)
      print('HF missing'    ,    __UpperCAmelCase							)
      print('HF unexpected_keys'    ,    __UpperCAmelCase							)

      # 5. Forward HF Data2VecVision model
      with torch.no_grad():
            SCREAMING_SNAKE_CASE_					=    hf_model(__UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_					=    hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

      # 6. Compare
      SCREAMING_SNAKE_CASE_					=    torch.max(torch.abs(hf_output - orig_model_output							)							).item()

      print(f"max_absolute_diff = {max_absolute_diff}"							)
      SCREAMING_SNAKE_CASE_					=    torch.allclose(__UpperCAmelCase    ,    __UpperCAmelCase    ,    atol=1E-3							)
      print('Do both models output the same tensors?'    ,    '🔥' if success else '💩'							)
      if not success:
            raise Exception('Something went wRoNg'							)

      # 7. Save
      print(f"Saving to {args.hf_checkpoint_name}"							)
      hf_model.save_pretrained(args.hf_checkpoint_name							)
      image_processor.save_pretrained(args.hf_checkpoint_name							)


if __name__ == "__main__":
   main()
   # Run the following to convert checkpoints
   #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
   #          --beit_checkpoint ./pretrained_base.pt \
   #          --hf_checkpoint_name "./data2vec-vision-base"
   #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
   #          --beit_checkpoint ./finetuned_base.pt \
   #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
   #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
   #          --beit_checkpoint ./pretrained_large.pt \
   #          --hf_checkpoint_name "./data2vec-vision-large"
   #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
   #          --beit_checkpoint ./finetuned_large.pt \
   #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"





import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowerCamelCase__				:     int			       =							False





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


 '''simple docstring'''







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



 def    lowerCAmelCase_						(							self						:	Optional[int]		):
       SCREAMING_SNAKE_CASE_					=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'		)
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(_lowerCAmelCase		)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'		)

       SCREAMING_SNAKE_CASE_					=    torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    pipe(
           prompt='first prompt'       ,       image=_lowerCAmelCase       ,       text_to_image_strength=0.75       ,       generator=_lowerCAmelCase       ,       guidance_scale=7.5       ,       num_inference_steps=2       ,       output_type='numpy'       ,       ).images

       with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_					=    VersatileDiffusionDualGuidedPipeline.from_pretrained(_lowerCAmelCase		)

       pipe.to(_lowerCAmelCase		)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    generator.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    pipe(
           prompt='first prompt'       ,       image=_lowerCAmelCase       ,       text_to_image_strength=0.75       ,       generator=_lowerCAmelCase       ,       guidance_scale=7.5       ,       num_inference_steps=2       ,       output_type='numpy'       ,       ).images

       assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"




 def    lowerCAmelCase_						(							self						:	Any		):
       SCREAMING_SNAKE_CASE_					=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'		)
       pipe.remove_unused_weights()
       pipe.to(_lowerCAmelCase		)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    'cyberpunk 2077'
       SCREAMING_SNAKE_CASE_					=    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'		)
       SCREAMING_SNAKE_CASE_					=    torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    pipe(
           prompt=_lowerCAmelCase       ,       image=_lowerCAmelCase       ,       text_to_image_strength=0.75       ,       generator=_lowerCAmelCase       ,       guidance_scale=7.5       ,       num_inference_steps=50       ,       output_type='numpy'       ,       ).images

       SCREAMING_SNAKE_CASE_					=    image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE_					=    np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]		)

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