



"""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     =    "\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> 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\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"
def        A_  (      _lowercase, _lowercase, _lowercase=8     ):







						'''simple docstring'''


						snake_case_			:Any						=      height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						snake_case_			:Optional[int]						=      width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor
def        A_  (      _lowercase, _lowercase=512, _lowercase=512     ):







						'''simple docstring'''


						snake_case_			:List[str]						=      pil_image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1     )
						snake_case_			:Union[str, Any]						=      np.array(pil_image.convert("""RGB"""     )     )
						snake_case_			:str						=      arr.astype(np.floataa     ) / 127.5 - 1
						snake_case_			:Dict						=      np.transpose(_lowercase, [2, 0, 1]     )
						snake_case_			:str						=      torch.from_numpy(_lowercase     ).unsqueeze(0     )
						return image

class     lowerCamelCase  (      _lowerCAmelCase    ):






					'''simple docstring'''





					def __init__(	self:							Union[str, Any]						,					snake_case:							UNetaDConditionModel						,					snake_case:							DDPMScheduler						,					snake_case:							VQModel						,					)       ->     int:
											super().__init__()

											self.register_modules(
											    unet=snake_case						,					scheduler=snake_case						,					movq=snake_case						,					)
											snake_case_			:List[Any]						=      2 ** (len(self.movq.config.block_out_channels						) - 1)






					def        lowerCAmelCase_    (	self:							Any						,					snake_case:							int						,					snake_case:							str						,					snake_case:							str						)       ->     List[Any]:
											# get the original timestep using init_timestep
											snake_case_			:Dict						=      min(int(num_inference_steps * strength						)						,					snake_case						)

											snake_case_			:Tuple						=      max(num_inference_steps - init_timestep						,					0						)
											snake_case_			:Union[str, Any]						=      self.scheduler.timesteps[t_start:]

											return timesteps, num_inference_steps - t_start






					def        lowerCAmelCase_    (	self:							Optional[int]						,					snake_case:							List[Any]						,					snake_case:							Dict						,					snake_case:							Optional[Any]						,					snake_case:							List[str]						,					snake_case:							Union[str, Any]						,					snake_case:							Optional[int]						,					snake_case:							Optional[Any]=None						)       ->     List[Any]:
											if not isinstance(snake_case						,					(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(snake_case						)}"""						)

											snake_case_			:Union[str, Any]						=      image.to(device=snake_case						,					dtype=snake_case						)

											snake_case_			:int						=      batch_size * num_images_per_prompt

											if image.shape[1] == 4:
																	snake_case_			:Tuple						=      image

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

																	elif isinstance(snake_case						,					snake_case						):
																							snake_case_			:Optional[int]						=      [
																							    self.movq.encode(image[i : i + 1]						).latent_dist.sample(generator[i]						) for i in range(snake_case						)
																							]
																							snake_case_			:List[str]						=      torch.cat(snake_case						,					dim=0						)
																	else:
																							snake_case_			:Optional[int]						=      self.movq.encode(snake_case						).latent_dist.sample(snake_case						)

																	snake_case_			:Optional[Any]						=      self.movq.config.scaling_factor * init_latents

											snake_case_			:int						=      torch.cat([init_latents]						,					dim=0						)

											snake_case_			:Tuple						=      init_latents.shape
											snake_case_			:str						=      randn_tensor(snake_case						,					generator=snake_case						,					device=snake_case						,					dtype=snake_case						)

											# get latents
											snake_case_			:int						=      self.scheduler.add_noise(snake_case						,					snake_case						,					snake_case						)

											snake_case_			:Tuple						=      init_latents

											return latents






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

											snake_case_			:Tuple						=      torch.device(f"""cuda:{gpu_id}"""						)

											snake_case_			:List[str]						=      [
											    self.unet,
											    self.movq,
											]
											for cpu_offloaded_model in models:
																	if cpu_offloaded_model is not None:
																							cpu_offload(snake_case						,					snake_case						)






					def        lowerCAmelCase_    (	self:							Optional[Any]						,					snake_case:							Optional[Any]=0						)       ->     Dict:
											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."""						)

											snake_case_			:Optional[Any]						=      torch.device(f"""cuda:{gpu_id}"""						)

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

											snake_case_			:int						=      None
											for cpu_offloaded_model in [self.unet, self.movq]:
																	snake_case_,	snake_case_			:Union[str, Any]						=      cpu_offload_with_hook(snake_case						,					snake_case						,					prev_module_hook=snake_case						)

											# We'll offload the last model manually.
											snake_case_			:Any						=      hook






					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def        lowerCAmelCase_    (	self:							Any						)       ->     Any:
											if not hasattr(self.unet						,					"""_hf_hook"""						):
																	return self.device
											for module in self.unet.modules():
																	if (
																	    hasattr(snake_case						,					"""_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(snake_case						)
					def __call__(	self:							Optional[int]						,					snake_case:							Union[torch.FloatTensor, List[torch.FloatTensor]]						,					snake_case:							Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]						,					snake_case:							Union[torch.FloatTensor, List[torch.FloatTensor]]						,					snake_case:							torch.FloatTensor						,					snake_case:							int = 512						,					snake_case:							int = 512						,					snake_case:							int = 100						,					snake_case:							float = 4.0						,					snake_case:							float = 0.3						,					snake_case:							int = 1						,					snake_case:							Optional[Union[torch.Generator, List[torch.Generator]]] = None						,					snake_case:							Optional[str] = "pil"						,					snake_case:							bool = True						,					)       ->     Any:
											snake_case_			:Optional[int]						=      self._execution_device

											snake_case_			:List[Any]						=      guidance_scale > 1.0

											if isinstance(snake_case						,					snake_case						):
																	snake_case_			:List[str]						=      torch.cat(snake_case						,					dim=0						)
											if isinstance(snake_case						,					snake_case						):
																	snake_case_			:List[str]						=      torch.cat(snake_case						,					dim=0						)
											if isinstance(snake_case						,					snake_case						):
																	snake_case_			:Optional[int]						=      torch.cat(snake_case						,					dim=0						)

											snake_case_			:Union[str, Any]						=      image_embeds.shape[0]

											if do_classifier_free_guidance:
																	snake_case_			:Optional[Any]						=      image_embeds.repeat_interleave(snake_case						,					dim=0						)
																	snake_case_			:List[str]						=      negative_image_embeds.repeat_interleave(snake_case						,					dim=0						)
																	snake_case_			:str						=      hint.repeat_interleave(snake_case						,					dim=0						)

											snake_case_			:Dict						=      torch.cat([negative_image_embeds, image_embeds]						,					dim=0						).to(dtype=self.unet.dtype						,					device=snake_case						)
											snake_case_			:int						=      torch.cat([hint, hint]						,					dim=0						).to(dtype=self.unet.dtype						,					device=snake_case						)

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

											snake_case_			:Optional[int]						=      torch.cat([prepare_image(snake_case						,					snake_case						,					snake_case						) for i in image]						,					dim=0						)
											snake_case_			:Any						=      image.to(dtype=image_embeds.dtype						,					device=snake_case						)

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

																	snake_case_			:Any						=      {"""image_embeds""": image_embeds, """hint""": hint}
																	snake_case_			:List[str]						=      self.unet(
																	    sample=snake_case						,					timestep=snake_case						,					encoder_hidden_states=snake_case						,					added_cond_kwargs=snake_case						,					return_dict=snake_case						,					)[0]

																	if do_classifier_free_guidance:
																							snake_case_,	snake_case_			:List[str]						=      noise_pred.split(latents.shape[1]						,					dim=1						)
																							snake_case_,	snake_case_			:Union[str, Any]						=      noise_pred.chunk(2						)
																							snake_case_,	snake_case_			:Any						=      variance_pred.chunk(2						)
																							snake_case_			:str						=      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																							snake_case_			:List[str]						=      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"]
																	):
																							snake_case_,	snake_case_			:str						=      noise_pred.split(latents.shape[1]						,					dim=1						)

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

																	snake_case_			:str						=      self.scheduler.step(
																	    snake_case						,					snake_case						,					snake_case						,					generator=snake_case						,					)[0]

											# post-processing
											snake_case_			:int						=      self.movq.decode(snake_case						,					force_not_quantize=snake_case						)["""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"]:
																	snake_case_			:Any						=      image * 0.5 + 0.5
																	snake_case_			:Any						=      image.clamp(0						,					1						)
																	snake_case_			:List[str]						=      image.cpu().permute(0						,					2						,					3						,					1						).float().numpy()

											if output_type == "pil":
																	snake_case_			:Any						=      self.numpy_to_pil(snake_case						)

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=snake_case						)





"""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        A_  (      _lowercase, _lowercase=False, _lowercase=False, _lowercase="data2vec."     ):







						'''simple docstring'''


						snake_case_			:List[str]						=      """backbone.""" if is_semantic else """"""

						snake_case_			:Tuple						=      []
						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        A_  (      _lowercase, _lowercase, _lowercase=False, _lowercase=False, _lowercase="data2vec_vision."     ):







						'''simple docstring'''


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

												snake_case_			:Any						=      in_proj_weight[
												    : config.hidden_size, :
												]
												snake_case_			:Union[str, Any]						=      q_bias
												snake_case_			:Optional[Any]						=      in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												snake_case_			:int						=      in_proj_weight[
												    -config.hidden_size :, :
												]
												snake_case_			:Any						=      v_bias

												# gamma_1 and gamma_2
												# we call them lambda because otherwise they are renamed when using .from_pretrained
												snake_case_			:int						=      state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""     )
												snake_case_			:List[Any]						=      state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""     )

												snake_case_			:str						=      gamma_a
												snake_case_			:Tuple						=      gamma_a

												# relative_position bias table + index
												if not has_lm_head:
																		# each layer has its own relative position bias
																		snake_case_			:List[Any]						=      state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table"""     )
																		snake_case_			:Union[str, Any]						=      state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index"""     )

																		snake_case_			:Tuple						=      table
																		snake_case_			:List[Any]						=      index
def        A_  (      ):







						'''simple docstring'''


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

						return parser.parse_args()
def        A_  (      _lowercase, _lowercase, _lowercase     ):







						'''simple docstring'''


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

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

												load(_lowercase, prefix=_lowercase     )

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

												snake_case_			:int						=      warn_missing_keys

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

						snake_case_			:Optional[int]						=      {
						    """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,
												    }     )

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

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

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

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

						load_state_dict(_lowercase, _lowercase, prefix=""""""     )

						return model
def        A_  (      ):







						'''simple docstring'''


						snake_case_			:int						=      get_args()

						snake_case_			:str						=      """ft1k""" in args.hf_checkpoint_name
						snake_case_			:Optional[int]						=      """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_			:Dict						=      DataaVecVisionConfig()
						if is_finetuned:
												snake_case_			:Optional[int]						=      True
												snake_case_			:List[str]						=      False
												snake_case_			:Dict						=      True
												snake_case_			:Union[str, Any]						=      1000

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

						if is_large:
												snake_case_			:Dict						=      1024
												snake_case_			:Union[str, Any]						=      4096
												snake_case_			:Optional[int]						=      24
												snake_case_			:Dict						=      16

						# 2. Load Beit model
						snake_case_			:Optional[Any]						=      load_beit_model(_lowercase, _lowercase, _lowercase     )
						orig_model.eval()

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

						snake_case_			:List[Any]						=      (pixel_values,) if is_finetuned else (pixel_values, None)
						with torch.no_grad():
												snake_case_			:str						=      orig_model(*_lowercase     )

						# 4. Load HF Data2VecVision model
						if is_finetuned:
												snake_case_			:Union[str, Any]						=      DataaVecVisionForImageClassification(_lowercase     )
												hf_model.eval()
												snake_case_			:List[Any]						=      False
												snake_case_			:Dict						=      """data2vec_vision."""
						else:
												snake_case_			:Tuple						=      DataaVecVisionModel(_lowercase     )
												hf_model.eval()
												snake_case_			:Dict						=      True
												snake_case_			:int						=      """"""

						snake_case_			:int						=      create_rename_keys(_lowercase, hf_prefix=_lowercase, has_lm_head=_lowercase     )
						snake_case_			:int						=      orig_model.state_dict()
						for src, dest in rename_keys:
												snake_case_			:Optional[int]						=      state_dict.pop(_lowercase     )
												snake_case_			:Union[str, Any]						=      val

						read_in_q_k_v(_lowercase, _lowercase, hf_prefix=_lowercase, has_lm_head=_lowercase     )
						snake_case_,	snake_case_			:List[Any]						=      hf_model.load_state_dict(_lowercase, strict=_lowercase     )
						print("""HF missing""", _lowercase     )
						print("""HF unexpected_keys""", _lowercase     )

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

						snake_case_			:Any						=      hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

						print(f"""max_absolute_diff = {max_absolute_diff}"""     )
						snake_case_			:str						=      torch.allclose(_lowercase, _lowercase, 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"
