"""simple docstring"""


from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


__A       			=      logging.get_logger(__name__)  # pylint: disable=invalid-name

__A       			=      """
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")

        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""

def 						UpperCamelCase				(	_lowerCAmelCase   :							Optional[int]				, _lowerCAmelCase   :							List[Any]				, _lowerCAmelCase   :							int=8						):
    __a			      =						height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    __a			      =						width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor

def 						UpperCamelCase				(	_lowerCAmelCase   :							Optional[int]				, _lowerCAmelCase   :							Any=512				, _lowerCAmelCase   :							Optional[int]=512						):
    __a			      =						pil_image.resize((w, h)				, resample=Image.BICUBIC				, reducing_gap=1						)
    __a			      =						np.array(pil_image.convert("""RGB"""						)						)
    __a			      =						arr.astype(np.floataa						) / 127.5 - 1
    __a			      =						np.transpose(_lowerCAmelCase				, [2, 0, 1]						)
    __a			      =						torch.from_numpy(_lowerCAmelCase						).unsqueeze(0						)
    return image






class 					a					(				A_							):



  def __init__(		self   :    Optional[int]						,  lowerCamelCase_   :    UNetaDConditionModel						,  lowerCamelCase_   :    DDPMScheduler						,  lowerCamelCase_   :    VQModel						,  )      -> List[str]:
      super().__init__()

      self.register_modules(
          unet=lowerCamelCase_						,  scheduler=lowerCamelCase_						,  movq=lowerCamelCase_						,  )
      __a			      =						2 ** (len(self.movq.config.block_out_channels     ) - 1)



  def        lowerCAmelCase_				(		self   :    Union[str, Any]						,  lowerCamelCase_   :    Tuple						,  lowerCamelCase_   :    Optional[int]						,  lowerCamelCase_   :    List[str]     )      -> int:
      # get the original timestep using init_timestep
      __a			      =						min(int(num_inference_steps * strength     )						,  lowerCamelCase_     )

      __a			      =						max(num_inference_steps - init_timestep						,  0     )
      __a			      =						self.scheduler.timesteps[t_start:]

      return timesteps, num_inference_steps - t_start



  def        lowerCAmelCase_				(		self   :    List[str]						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    List[Any]						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    List[Any]						,  lowerCamelCase_   :    Dict						,  lowerCamelCase_   :    Any						,  lowerCamelCase_   :    List[Any]=None     )      -> Optional[int]:
      if not isinstance(lowerCamelCase_						,  (torch.Tensor, PIL.Image.Image, list)     ):
          raise ValueError(
              F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCamelCase_     )}"""     )

      __a			      =						image.to(device=lowerCamelCase_						,  dtype=lowerCamelCase_     )

      __a			      =						batch_size * num_images_per_prompt

      if image.shape[1] == 4:
          __a			      =						image

      else:
          if isinstance(lowerCamelCase_						,  lowerCamelCase_     ) and len(lowerCamelCase_     ) != batch_size:
              raise ValueError(
                  F"""You have passed a list of generators of length {len(lowerCamelCase_     )}, but requested an effective batch"""
                  F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""     )

          elif isinstance(lowerCamelCase_						,  lowerCamelCase_     ):
              __a			      =						[
                  self.movq.encode(image[i : i + 1]     ).latent_dist.sample(generator[i]     ) for i in range(lowerCamelCase_     )
              ]
              __a			      =						torch.cat(lowerCamelCase_						,  dim=0     )
          else:
              __a			      =						self.movq.encode(lowerCamelCase_     ).latent_dist.sample(lowerCamelCase_     )

          __a			      =						self.movq.config.scaling_factor * init_latents

      __a			      =						torch.cat([init_latents]						,  dim=0     )

      __a			      =						init_latents.shape
      __a			      =						randn_tensor(lowerCamelCase_						,  generator=lowerCamelCase_						,  device=lowerCamelCase_						,  dtype=lowerCamelCase_     )

      # get latents
      __a			      =						self.scheduler.add_noise(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_     )

      __a			      =						init_latents

      return latents



  def        lowerCAmelCase_				(		self   :    Any						,  lowerCamelCase_   :    List[str]=0     )      -> Union[str, Any]:
      if is_accelerate_available():
          from accelerate import cpu_offload
      else:
          raise ImportError("""Please install accelerate via `pip install accelerate`"""     )

      __a			      =						torch.device(F"""cuda:{gpu_id}"""     )

      __a			      =						[
          self.unet,
          self.movq,
      ]
      for cpu_offloaded_model in models:
          if cpu_offloaded_model is not None:
              cpu_offload(lowerCamelCase_						,  lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    List[Any]						,  lowerCamelCase_   :    int=0     )      -> Any:
      if is_accelerate_available() and is_accelerate_version(""">="""						,  """0.17.0.dev0"""     ):
          from accelerate import cpu_offload_with_hook
      else:
          raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""     )

      __a			      =						torch.device(F"""cuda:{gpu_id}"""     )

      if self.device.type != "cpu":
          self.to("""cpu"""						,  silence_dtype_warnings=lowerCamelCase_     )
          torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

      __a			      =						None
      for cpu_offloaded_model in [self.unet, self.movq]:
          __a      ,  __a			      =						cpu_offload_with_hook(lowerCamelCase_						,  lowerCamelCase_						,  prev_module_hook=lowerCamelCase_     )

      # We'll offload the last model manually.
      __a			      =						hook



  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def        lowerCAmelCase_				(		self   :    str     )      -> Tuple:
      if not hasattr(self.unet						,  """_hf_hook"""     ):
          return self.device
      for module in self.unet.modules():
          if (
              hasattr(lowerCamelCase_						,  """_hf_hook"""     )
              and hasattr(module._hf_hook						,  """execution_device"""     )
              and module._hf_hook.execution_device is not None
          ):
              return torch.device(module._hf_hook.execution_device     )
      return self.device



  @torch.no_grad()
  @replace_example_docstring(lowerCamelCase_     )
  def __call__(		self   :    List[Any]						,  lowerCamelCase_   :    Union[torch.FloatTensor, List[torch.FloatTensor]]						,  lowerCamelCase_   :    Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]						,  lowerCamelCase_   :    Union[torch.FloatTensor, List[torch.FloatTensor]]						,  lowerCamelCase_   :    torch.FloatTensor						,  lowerCamelCase_   :    int = 5_12						,  lowerCamelCase_   :    int = 5_12						,  lowerCamelCase_   :    int = 1_00						,  lowerCamelCase_   :    float = 4.0						,  lowerCamelCase_   :    float = 0.3						,  lowerCamelCase_   :    int = 1						,  lowerCamelCase_   :    Optional[Union[torch.Generator, List[torch.Generator]]] = None						,  lowerCamelCase_   :    Optional[str] = "pil"						,  lowerCamelCase_   :    bool = True						,  )      -> List[Any]:
      __a			      =						self._execution_device

      __a			      =						guidance_scale > 1.0

      if isinstance(lowerCamelCase_						,  lowerCamelCase_     ):
          __a			      =						torch.cat(lowerCamelCase_						,  dim=0     )
      if isinstance(lowerCamelCase_						,  lowerCamelCase_     ):
          __a			      =						torch.cat(lowerCamelCase_						,  dim=0     )
      if isinstance(lowerCamelCase_						,  lowerCamelCase_     ):
          __a			      =						torch.cat(lowerCamelCase_						,  dim=0     )

      __a			      =						image_embeds.shape[0]

      if do_classifier_free_guidance:
          __a			      =						image_embeds.repeat_interleave(lowerCamelCase_						,  dim=0     )
          __a			      =						negative_image_embeds.repeat_interleave(lowerCamelCase_						,  dim=0     )
          __a			      =						hint.repeat_interleave(lowerCamelCase_						,  dim=0     )

      __a			      =						torch.cat([negative_image_embeds, image_embeds]						,  dim=0     ).to(dtype=self.unet.dtype						,  device=lowerCamelCase_     )
      __a			      =						torch.cat([hint, hint]						,  dim=0     ).to(dtype=self.unet.dtype						,  device=lowerCamelCase_     )

      if not isinstance(lowerCamelCase_						,  lowerCamelCase_     ):
          __a			      =						[image]
      if not all(isinstance(lowerCamelCase_						,  (PIL.Image.Image, torch.Tensor)     ) for i in image     ):
          raise ValueError(
              F"""Input is in incorrect format: {[type(lowerCamelCase_     ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""     )

      __a			      =						torch.cat([prepare_image(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_     ) for i in image]						,  dim=0     )
      __a			      =						image.to(dtype=image_embeds.dtype						,  device=lowerCamelCase_     )

      __a			      =						self.movq.encode(lowerCamelCase_     )["""latents"""]
      __a			      =						latents.repeat_interleave(lowerCamelCase_						,  dim=0     )
      self.scheduler.set_timesteps(lowerCamelCase_						,  device=lowerCamelCase_     )
      __a      ,  __a			      =						self.get_timesteps(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_     )
      __a			      =						timesteps[:1].repeat(batch_size * num_images_per_prompt     )
      __a      ,  __a			      =						downscale_height_and_width(lowerCamelCase_						,  lowerCamelCase_						,  self.movq_scale_factor     )
      __a			      =						self.prepare_latents(
          lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  image_embeds.dtype						,  lowerCamelCase_						,  lowerCamelCase_     )
      for i, t in enumerate(self.progress_bar(lowerCamelCase_     )     ):
          # expand the latents if we are doing classifier free guidance
          __a			      =						torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents

          __a			      =						{"""image_embeds""": image_embeds, """hint""": hint}
          __a			      =						self.unet(
              sample=lowerCamelCase_						,  timestep=lowerCamelCase_						,  encoder_hidden_states=lowerCamelCase_						,  added_cond_kwargs=lowerCamelCase_						,  return_dict=lowerCamelCase_						,  )[0]

          if do_classifier_free_guidance:
              __a      ,  __a			      =						noise_pred.split(latents.shape[1]						,  dim=1     )
              __a      ,  __a			      =						noise_pred.chunk(2     )
              __a      ,  __a			      =						variance_pred.chunk(2     )
              __a			      =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
              __a			      =						torch.cat([noise_pred, variance_pred_text]						,  dim=1     )

          if not (
              hasattr(self.scheduler.config						,  """variance_type"""     )
              and self.scheduler.config.variance_type in ["learned", "learned_range"]
          ):
              __a      ,  __a			      =						noise_pred.split(latents.shape[1]						,  dim=1     )

          # compute the previous noisy sample x_t -> x_t-1

          __a			      =						self.scheduler.step(
              lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  generator=lowerCamelCase_						,  )[0]

      # post-processing
      __a			      =						self.movq.decode(lowerCamelCase_						,  force_not_quantize=lowerCamelCase_     )["""sample"""]

      if output_type not in ["pt", "np", "pil"]:
          raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""     )

      if output_type in ["np", "pil"]:
          __a			      =						image * 0.5 + 0.5
          __a			      =						image.clamp(0						,  1     )
          __a			      =						image.cpu().permute(0						,  2						,  3						,  1     ).float().numpy()

      if output_type == "pil":
          __a			      =						self.numpy_to_pil(lowerCamelCase_     )

      if not return_dict:
          return (image,)

      return ImagePipelineOutput(images=lowerCamelCase_     )

"""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 						UpperCamelCase				(	_lowerCAmelCase   :							List[str]				, _lowerCAmelCase   :							Optional[int]=False				, _lowerCAmelCase   :							Union[str, Any]=False				, _lowerCAmelCase   :							Any="data2vec."						):
    __a			      =						"""backbone.""" if is_semantic else """"""

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

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

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

        __a			      =						gamma_a
        __a			      =						gamma_a

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

            __a			      =						table
            __a			      =						index

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

    return parser.parse_args()

def 						UpperCamelCase				(	_lowerCAmelCase   :							Optional[int]				, _lowerCAmelCase   :							Union[str, Any]				, _lowerCAmelCase   :							Union[str, Any]						):
    def load_state_dict(_lowerCAmelCase   :							Tuple				, _lowerCAmelCase   :							str				, _lowerCAmelCase   :							Union[str, Any]=""				, _lowerCAmelCase   :							Union[str, Any]="relative_position_index"						):
        __a			      =						[]
        __a			      =						[]
        __a			      =						[]
        # copy state_dict so _load_from_state_dict can modify it
        __a			      =						getattr(_lowerCAmelCase				, """_metadata"""				, _lowerCAmelCase						)
        __a			      =						state_dict.copy()
        if metadata is not None:
            __a			      =						metadata

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

        load(_lowerCAmelCase				, prefix=_lowerCAmelCase						)

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

        __a			      =						warn_missing_keys

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

    __a			      =						{
        """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""": 1000,
                """use_mean_pooling""": True,
                """init_scale""": 0.001,
                """use_rel_pos_bias""": True,
            }						)

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

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

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

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

    load_state_dict(_lowerCAmelCase				, _lowerCAmelCase				, prefix=""""""						)

    return model

def 						UpperCamelCase				(	):
    __a			      =						get_args()

    __a			      =						"""ft1k""" in args.hf_checkpoint_name
    __a			      =						"""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
    __a			      =						DataaVecVisionConfig()
    if is_finetuned:
        __a			      =						True
        __a			      =						False
        __a			      =						True
        __a			      =						1000

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

    if is_large:
        __a			      =						1024
        __a			      =						4096
        __a			      =						24
        __a			      =						16

    # 2. Load Beit model
    __a			      =						load_beit_model(_lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase						)
    orig_model.eval()

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

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

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

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

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

    # 5. Forward HF Data2VecVision model
    with torch.no_grad():
        __a			      =						hf_model(_lowerCAmelCase						)

    __a			      =						hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

    print(f"""max_absolute_diff = {max_absolute_diff}"""						)
    __a			      =						torch.allclose(_lowerCAmelCase				, _lowerCAmelCase				, 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"
