
'''simple docstring'''





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 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				List[str]=False			,       _a  :				List[Any]=False			,       _a  :				int="data2vec."      ):
      snake_case_    							=				"backbone." if is_semantic else ""

      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 	snake_case_							(				_a  :				List[str]			,       _a  :				Optional[int]			,       _a  :				Dict=False			,       _a  :				Union[str, Any]=False			,       _a  :				List[Any]="data2vec_vision."      ):
      for i in range(config.num_hidden_layers      ):
            snake_case_    							=				"backbone." if is_semantic else ""
            # queries, keys and values
            snake_case_    							=				state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''      )
            snake_case_    							=				state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''      )
            snake_case_    							=				state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''      )

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

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

            snake_case_    							=				gamma_a
            snake_case_    							=				gamma_a

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

                  snake_case_    							=				table
                  snake_case_    							=				index
def 	snake_case_							(				):
      snake_case_    							=				argparse.ArgumentParser(
          "Convert Data2VecVision to HF for image classification and pretraining"			,       add_help=_a      )
      parser.add_argument("--hf_checkpoint_name"			,       type=_a      )
      parser.add_argument("--input_size"			,       default=224			,       type=_a			,       help="images input size"      )
      parser.add_argument("--beit_checkpoint"			,       default=""			,       help="beit checkpoint"      )

      return parser.parse_args()
def 	snake_case_							(				_a  :				Tuple			,       _a  :				Optional[int]			,       _a  :				str      ):
      def load_state_dict(_a  :				Union[str, Any]			,       _a  :				Union[str, Any]			,       _a  :				List[str]=""			,       _a  :				Dict="relative_position_index"      ):
            snake_case_    							=				[]
            snake_case_    							=				[]
            snake_case_    							=				[]
            # copy state_dict so _load_from_state_dict can modify it
            snake_case_    							=				getattr(_a			,       "_metadata"			,       _a      )
            snake_case_    							=				state_dict.copy()
            if metadata is not None:
                  snake_case_    							=				metadata

            def load(_a  :				List[Any]			,       _a  :				Dict=""      ):
                  snake_case_    							=				{} if metadata is None else metadata.get(prefix[:-1]			,       {}      )
                  module._load_from_state_dict(
                      _a			,       _a			,       _a			,       _a			,       _a			,       _a			,       _a      )
                  for name, child in module._modules.items():
                        if child is not None:
                              load(_a			,       prefix + name + "."      )

            load(_a			,       prefix=_a      )

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

            snake_case_    							=				warn_missing_keys

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

      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": 1_000,
                    "use_mean_pooling": True,
                    "init_scale": 0.0_0_1,
                    "use_rel_pos_bias": True,
                }      )

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

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

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

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

      load_state_dict(_a			,       _a			,       prefix=""      )

      return model
def 	snake_case_							(				):
      snake_case_    							=				get_args()

      snake_case_    							=				"ft1k" in args.hf_checkpoint_name
      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
      snake_case_    							=				DataaVecVisionConfig()
      if is_finetuned:
            snake_case_    							=				True
            snake_case_    							=				False
            snake_case_    							=				True
            snake_case_    							=				1_000

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

      if is_large:
            snake_case_    							=				1_024
            snake_case_    							=				4_096
            snake_case_    							=				24
            snake_case_    							=				16

      # 2. Load Beit model
      snake_case_    							=				load_beit_model(_a			,       _a			,       _a      )
      orig_model.eval()

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

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

      # 4. Load HF Data2VecVision model
      if is_finetuned:
            snake_case_    							=				DataaVecVisionForImageClassification(_a      )
            hf_model.eval()
            snake_case_    							=				False
            snake_case_    							=				"data2vec_vision."
      else:
            snake_case_    							=				DataaVecVisionModel(_a      )
            hf_model.eval()
            snake_case_    							=				True
            snake_case_    							=				""

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

      read_in_q_k_v(_a			,       _a			,       hf_prefix=_a			,       has_lm_head=_a      )
      snake_case_							,						snake_case_    							=				hf_model.load_state_dict(_a			,       strict=_a      )
      print("HF missing"			,       _a      )
      print("HF unexpected_keys"			,       _a      )

      # 5. Forward HF Data2VecVision model
      with torch.no_grad():
            snake_case_    							=				hf_model(_a      )

      snake_case_    							=				hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

      print(F'''max_absolute_diff = {max_absolute_diff}'''      )
      snake_case_    							=				torch.allclose(_a			,       _a			,       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"



'''simple docstring'''





from .pipeline_kandinskya_a import KandinskyVaaPipeline
from .pipeline_kandinsky2_2_controlnet import KandinskyVaaControlnetPipeline
from .pipeline_kandinsky2_2_controlnet_img2img import KandinskyVaaControlnetImgaImgPipeline
from .pipeline_kandinsky2_2_img2img import KandinskyVaaImgaImgPipeline
from .pipeline_kandinsky2_2_inpainting import KandinskyVaaInpaintPipeline
from .pipeline_kandinsky2_2_prior import KandinskyVaaPriorPipeline
from .pipeline_kandinsky2_2_prior_emb2emb import KandinskyVaaPriorEmbaEmbPipeline

